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Available online , doi: 10.11999/JEIT230882
Abstract:
Tomographic Synthetic Aperture Radar (TomoSAR) can effectively recover the information of ground objects in steep terrain, and is one of the research hotspots in urban mapping. However, the current public data sets lack the true values of the object models, and cannot quantitatively verify the TomoSAR algorithm. To solve this problem and further promote the development of TomoSAR technology, this paper first proposes an Ray Tracing Based Space Borne Radar Advanced Simulator (RT-SBRAS), which can quickly and stably simulate the spaceborne SAR images of complex buildings compared with previous methods. Based on this, the 1.0 version of the 3D SAR Building Simulation (3DSARBuSim) data set is constructed, which contains the full-link simulation data of eight typical building scenes in dual-band and multi-pass. Finally, Orthogonal Matching Pursuit (OMP) and dual-frequency OMP algorithms are verified on the proposed data set, and the data set can provide clear and accurate quantitative comparison for the algorithms.
Tomographic Synthetic Aperture Radar (TomoSAR) can effectively recover the information of ground objects in steep terrain, and is one of the research hotspots in urban mapping. However, the current public data sets lack the true values of the object models, and cannot quantitatively verify the TomoSAR algorithm. To solve this problem and further promote the development of TomoSAR technology, this paper first proposes an Ray Tracing Based Space Borne Radar Advanced Simulator (RT-SBRAS), which can quickly and stably simulate the spaceborne SAR images of complex buildings compared with previous methods. Based on this, the 1.0 version of the 3D SAR Building Simulation (3DSARBuSim) data set is constructed, which contains the full-link simulation data of eight typical building scenes in dual-band and multi-pass. Finally, Orthogonal Matching Pursuit (OMP) and dual-frequency OMP algorithms are verified on the proposed data set, and the data set can provide clear and accurate quantitative comparison for the algorithms.
Available online , doi: 10.11999/JEIT231036
Abstract:
The multi-functional integrated platform with simultaneous transmit and receive capability faces the strong Self-Interference (SI) coupled between the adjacent transmit and receive arrays. In this paper, a wideband SI cancellation method in the space domain for fully digital phased array systems is designed. A non-convex optimization problem is formulated to minimize the residual SI and noise power while constraining the loss of beamforming gain in the desired direction, and an alternate optimization method is proposed to jointly determine the transmit and receive beamforming weights, and the SI cancellation performance of the proposed algorithm is analyzed. Theoretical analysis and simulation results show that a 60-element array can achieve an Effective Isotropic Isolation (EII) of 168 dB when the central frequency is 2.4 GHz, the bandwidth is 100 MHz, and the beamforming gain loss is limited to 3 dB, which is 7 dB below the EII upper bound.
The multi-functional integrated platform with simultaneous transmit and receive capability faces the strong Self-Interference (SI) coupled between the adjacent transmit and receive arrays. In this paper, a wideband SI cancellation method in the space domain for fully digital phased array systems is designed. A non-convex optimization problem is formulated to minimize the residual SI and noise power while constraining the loss of beamforming gain in the desired direction, and an alternate optimization method is proposed to jointly determine the transmit and receive beamforming weights, and the SI cancellation performance of the proposed algorithm is analyzed. Theoretical analysis and simulation results show that a 60-element array can achieve an Effective Isotropic Isolation (EII) of 168 dB when the central frequency is 2.4 GHz, the bandwidth is 100 MHz, and the beamforming gain loss is limited to 3 dB, which is 7 dB below the EII upper bound.
Available online , doi: 10.11999/JEIT231209
Abstract:
HyperSpectral Image (HSI) classification is one of the most prominent research topics in geoscience and remote sensing image processing tasks. In recent years, the combination of Convolutional Neural Network (CNN) and vision transformer has achieved success in HSI classification tasks by comprehensively considering local-global information. Nevertheless, the ground objects of HSIs vary in scale, containing rich texture information and complex structures. The current methods based on the combination of CNN and vision transformer usually have limited capability to extract texture and structural information of multi-scale ground objects. To overcome the above limitations, a CNN and vision transformer-driven cross-layer multi-scale fusion network is proposed for HSI classification. Firstly, from the perspective of combining CNN and visual transformer, a cross-layer multi-scale local-global feature extraction module branch is constructed, which is composed of a convolution embedded vision transformer architecture and a cross-layer feature fusion module. Specifically, to enhance attention to multi-scale ground objects in HSIs, the convolution embedded vision transformer captures multi-scale local-global features effectively by organically combining multi-scale CNN and vision transformer. Furthermore, the cross-layer feature fusion module aggregates hierarchical multi-scale local-global features, thereby combining shallow texture information and deep structural information of ground objects. Secondly, a group multi-scale convolution module branch is designed to explore the potential multi-scale features from abundant spectral bands in HSIs. Finally, to mine local spectral details and global spectral information in HSIs, a residual group convolution module is designed to extract local-global spectral features. Experimental results on Indian Pines, Houston 2013, and Salinas Valley datasets confirm the effectiveness of the proposed method.
HyperSpectral Image (HSI) classification is one of the most prominent research topics in geoscience and remote sensing image processing tasks. In recent years, the combination of Convolutional Neural Network (CNN) and vision transformer has achieved success in HSI classification tasks by comprehensively considering local-global information. Nevertheless, the ground objects of HSIs vary in scale, containing rich texture information and complex structures. The current methods based on the combination of CNN and vision transformer usually have limited capability to extract texture and structural information of multi-scale ground objects. To overcome the above limitations, a CNN and vision transformer-driven cross-layer multi-scale fusion network is proposed for HSI classification. Firstly, from the perspective of combining CNN and visual transformer, a cross-layer multi-scale local-global feature extraction module branch is constructed, which is composed of a convolution embedded vision transformer architecture and a cross-layer feature fusion module. Specifically, to enhance attention to multi-scale ground objects in HSIs, the convolution embedded vision transformer captures multi-scale local-global features effectively by organically combining multi-scale CNN and vision transformer. Furthermore, the cross-layer feature fusion module aggregates hierarchical multi-scale local-global features, thereby combining shallow texture information and deep structural information of ground objects. Secondly, a group multi-scale convolution module branch is designed to explore the potential multi-scale features from abundant spectral bands in HSIs. Finally, to mine local spectral details and global spectral information in HSIs, a residual group convolution module is designed to extract local-global spectral features. Experimental results on Indian Pines, Houston 2013, and Salinas Valley datasets confirm the effectiveness of the proposed method.
Available online , doi: 10.11999/JEIT231389
Abstract:
Benefiting from rich spectrum and lamps, Integrated Visible Light Communication and Positioning (IVLCP) systems provide powerful technological solution to meet the high performance communication and positioning in indoor wireless networks. Meanwhile, the massive Multiple Input Multiple Output (m-MIMO) effectively enhance both service coverage and quality of IVLCP systems. However, the channel environment is more complex and the priori information rapidly changed in the m-MIMO-enabled IVLCP systems, making traditional methods challenging for fast and accurate channel estimation and positioning. In order to tackle this challenge, a Channel State Information Restoration and Positioning (CSIRP) network is proposed in this paper. The network not only effectively captures complex distribution feature of channel but also addressing the temporal variations in location, thereby enhancing the robustness and dynamic adaptability of channel and location estimation. Specifically, the CSIRP network employs a conditional generative adversarial process to adaptively train the generator and discriminatorr and thus achieves the channel estimation from received signals. Then, the Long Short-Term Memory(LSTM) is introduced to estimate the location of the receiver from the estimated channel. Simulation results demonstrate that the accuracy of both channel and location estimation achieved by the proposed CSIRP network outperforms existing deep learning benchmark schemes. This provides m-MIMO-enabled IVLCP systems with more reliable and accurate channel state information and positioning.
Benefiting from rich spectrum and lamps, Integrated Visible Light Communication and Positioning (IVLCP) systems provide powerful technological solution to meet the high performance communication and positioning in indoor wireless networks. Meanwhile, the massive Multiple Input Multiple Output (m-MIMO) effectively enhance both service coverage and quality of IVLCP systems. However, the channel environment is more complex and the priori information rapidly changed in the m-MIMO-enabled IVLCP systems, making traditional methods challenging for fast and accurate channel estimation and positioning. In order to tackle this challenge, a Channel State Information Restoration and Positioning (CSIRP) network is proposed in this paper. The network not only effectively captures complex distribution feature of channel but also addressing the temporal variations in location, thereby enhancing the robustness and dynamic adaptability of channel and location estimation. Specifically, the CSIRP network employs a conditional generative adversarial process to adaptively train the generator and discriminatorr and thus achieves the channel estimation from received signals. Then, the Long Short-Term Memory(LSTM) is introduced to estimate the location of the receiver from the estimated channel. Simulation results demonstrate that the accuracy of both channel and location estimation achieved by the proposed CSIRP network outperforms existing deep learning benchmark schemes. This provides m-MIMO-enabled IVLCP systems with more reliable and accurate channel state information and positioning.
Available online , doi: 10.11999/JEIT231195
Abstract:
The rapid development of swarm intelligence, represented by evolutionary computation, has triggered a new wave of technological transformation in the field of artificial intelligence. To meet the diverse application needs of complex systems, artificial intelligence is increasingly moving towards cross-level intelligent and collaborative research. In this paper, the concept of swarm intelligence cooperation oriented towards evolutionary computation is proposed. Based on the hierarchical levels of swarm intelligence cooperation, artificial intelligence research across different levels is categorized into micro-level cooperation, meso-level cooperation, and macro-level cooperation. From the perspective of swarm intelligence cooperation, a summary is provided on recent research in the aforementioned branches. Firstly, the micro-level cooperation is discussed by analyzing decision variable level cooperation and global/local level cooperation. Secondly, the meso-level cooperation is summarized from the dimensions of objective-level cooperation and task-level cooperation. Furthermore, an analysis of macro-level cooperation is conducted through the examination of space-air-ground-sea cooperation, vehicle-road-cloud cooperation, and edge-cloud cooperation in intelligent collaborative systems. Finally, the research challenges in the field of swarm intelligence cooperation oriented towards evolutionary computation are identified, and future directions for related fields are proposed.
The rapid development of swarm intelligence, represented by evolutionary computation, has triggered a new wave of technological transformation in the field of artificial intelligence. To meet the diverse application needs of complex systems, artificial intelligence is increasingly moving towards cross-level intelligent and collaborative research. In this paper, the concept of swarm intelligence cooperation oriented towards evolutionary computation is proposed. Based on the hierarchical levels of swarm intelligence cooperation, artificial intelligence research across different levels is categorized into micro-level cooperation, meso-level cooperation, and macro-level cooperation. From the perspective of swarm intelligence cooperation, a summary is provided on recent research in the aforementioned branches. Firstly, the micro-level cooperation is discussed by analyzing decision variable level cooperation and global/local level cooperation. Secondly, the meso-level cooperation is summarized from the dimensions of objective-level cooperation and task-level cooperation. Furthermore, an analysis of macro-level cooperation is conducted through the examination of space-air-ground-sea cooperation, vehicle-road-cloud cooperation, and edge-cloud cooperation in intelligent collaborative systems. Finally, the research challenges in the field of swarm intelligence cooperation oriented towards evolutionary computation are identified, and future directions for related fields are proposed.
Available online , doi: 10.11999/JEIT230508
Abstract:
The olfactory and visual neural systems in Drosophila are highly sensitive to the olfactory and visual stimuli in the natural environment. The highly sensitive single-modal perception and cross-modal collaboration decision-making mechanisms of the olfactory and visual neural systems provide certain inspiration for bionic applications. Firstly, based on the olfactory and visual neural systems in Drosophila, the current research status of the physiological mechanisms and computational models of single-modal perception decision-making of the olfactory and visual neurons is summarized. The summary is divided into three parts: capturing, processing, and decision-making of the olfactory and visual signals. Meanwhile, the physiological mechanisms and computational models of cross-modal collaboration decision-making of the olfactory and visual neurons in Drosophila are further expounded. Then, the typical bionic applications of single-modal perception and cross-modal collaboration in Drosophila are summarized. Finally, the current challenges of the physiological mechanisms and computational models of the olfactory and visual neural pathways in Drosophila are summarized and the future development trends are outlook for, which lays a foundation for future research work.
The olfactory and visual neural systems in Drosophila are highly sensitive to the olfactory and visual stimuli in the natural environment. The highly sensitive single-modal perception and cross-modal collaboration decision-making mechanisms of the olfactory and visual neural systems provide certain inspiration for bionic applications. Firstly, based on the olfactory and visual neural systems in Drosophila, the current research status of the physiological mechanisms and computational models of single-modal perception decision-making of the olfactory and visual neurons is summarized. The summary is divided into three parts: capturing, processing, and decision-making of the olfactory and visual signals. Meanwhile, the physiological mechanisms and computational models of cross-modal collaboration decision-making of the olfactory and visual neurons in Drosophila are further expounded. Then, the typical bionic applications of single-modal perception and cross-modal collaboration in Drosophila are summarized. Finally, the current challenges of the physiological mechanisms and computational models of the olfactory and visual neural pathways in Drosophila are summarized and the future development trends are outlook for, which lays a foundation for future research work.
Available online , doi: 10.11999/JEIT240010
Abstract:
Memristors have a natural plasticity that enables silicon-based neurons and nano-synapses with similar or the same mechanisms as biological neurons and synapses. Using a memristor as a synapse to couple two heterogeneous memristive cellular neural networks, a memristor-coupled heterogeneous cellular neural network is constructed in this paper. The coupled network contains a space equilibrium set related to the initial value conditions of memristor synapse and subnets, which can exhibit complex dynamic evolution. The multi-stable behaviors of the coupling network, such as stable point, period, chaos, hyperchaos and unbounded oscillation, which depend on the initial value conditions, are revealed by numerical simulation method. In addition, under the control of memristor synapse, two heterogeneous subnets can achieve phase synchronization. Finally, the experimental verification of the circuit is completed based on STM32 MCU hardware platform.
Memristors have a natural plasticity that enables silicon-based neurons and nano-synapses with similar or the same mechanisms as biological neurons and synapses. Using a memristor as a synapse to couple two heterogeneous memristive cellular neural networks, a memristor-coupled heterogeneous cellular neural network is constructed in this paper. The coupled network contains a space equilibrium set related to the initial value conditions of memristor synapse and subnets, which can exhibit complex dynamic evolution. The multi-stable behaviors of the coupling network, such as stable point, period, chaos, hyperchaos and unbounded oscillation, which depend on the initial value conditions, are revealed by numerical simulation method. In addition, under the control of memristor synapse, two heterogeneous subnets can achieve phase synchronization. Finally, the experimental verification of the circuit is completed based on STM32 MCU hardware platform.
Available online , doi: 10.11999/JEIT231133
Abstract:
In the future communication network, the sixth generation mobile communication system technology(6G), which is widely expected, will face many challenges, including the issue of ultra-reliable communication in high-speed mobile scenarios. Orthogonal Time Frequency Space (OTFS) modulation technology overcomes the multi-path and Doppler effects of traditional communication systems in high-speed mobile environments, and provides a new possibility for realizing 6G ultra-reliable communication. This paper first introduces the basic principle, mathematical model, interference and advantage analysis of OTFS. Then, the research status of OTFS technology in synchronization, channel estimation and signal detection is summarized and analyzed. Then, the application trend of OTFS is analyzed from four typical application scenarios of vehicle networking, unmanned aerial vehicle, satellite communication and marine communication. Finally, the difficulties and challenges to be overcome in future OTFS research are discussed from four aspects: reducing multi-dimensional matching filter, phase demodulation and channel estimation, hardware implementation complexity and improving the high utilization of time-frequency resources.
In the future communication network, the sixth generation mobile communication system technology(6G), which is widely expected, will face many challenges, including the issue of ultra-reliable communication in high-speed mobile scenarios. Orthogonal Time Frequency Space (OTFS) modulation technology overcomes the multi-path and Doppler effects of traditional communication systems in high-speed mobile environments, and provides a new possibility for realizing 6G ultra-reliable communication. This paper first introduces the basic principle, mathematical model, interference and advantage analysis of OTFS. Then, the research status of OTFS technology in synchronization, channel estimation and signal detection is summarized and analyzed. Then, the application trend of OTFS is analyzed from four typical application scenarios of vehicle networking, unmanned aerial vehicle, satellite communication and marine communication. Finally, the difficulties and challenges to be overcome in future OTFS research are discussed from four aspects: reducing multi-dimensional matching filter, phase demodulation and channel estimation, hardware implementation complexity and improving the high utilization of time-frequency resources.
Available online , doi: 10.11999/JEIT231236
Abstract:
In order to achieve identification of radar emitter unaffected by signal parameters and modulation methods, a method based on Dual Radio Frequency Fingerprint Convolutional Neural Network(Dual RFF-CNN2) neand feature fusion is proposed in this paper. Firstly, Raw-I/Q signals are extracted from the received radio frequency signals. Secondly, Axially Integral Bispectrum(AIB) and Square Integral Bispectrum (SIB) dimensionality reduction are performed separately on Raw-In-phase/Quadrature(Raw-I/Q)signals to construct the bispectrum integration matrix. Finally, both the Raw-I/Q signals and the bispectrum integration matrix are fed into the Dual RFF-CNN2 network for feature fusion to achieve identification of radar emitter. Experimental results demonstrate that this method achieves high identification accuracy, and the extracted "fingerprint features" exhibit stability and robustness.
In order to achieve identification of radar emitter unaffected by signal parameters and modulation methods, a method based on Dual Radio Frequency Fingerprint Convolutional Neural Network(Dual RFF-CNN2) neand feature fusion is proposed in this paper. Firstly, Raw-I/Q signals are extracted from the received radio frequency signals. Secondly, Axially Integral Bispectrum(AIB) and Square Integral Bispectrum (SIB) dimensionality reduction are performed separately on Raw-In-phase/Quadrature(Raw-I/Q)signals to construct the bispectrum integration matrix. Finally, both the Raw-I/Q signals and the bispectrum integration matrix are fed into the Dual RFF-CNN2 network for feature fusion to achieve identification of radar emitter. Experimental results demonstrate that this method achieves high identification accuracy, and the extracted "fingerprint features" exhibit stability and robustness.
Available online , doi: 10.11999/JEIT231365
Abstract:
Understanding the species composition, abundance and temporal and spatial distribution of fish on a global scale will help their biodiversity conservation. Underwater image acquisition is one of the main means to survey fish species diversity, but image data analysis is time-consuming and labor-intensive. Since 2015, a series of progress has been made in updating the datasets of marine fish images and optimizing the algorithm of deep learning models, but the performance of fine-grained classification is still insufficient, and the production practice application of research results is relatively weak. Therefore, the need for automated fish image classification in marine investigations is firstly studied. Then a comprehensive introduction to fish image datasets and deep learning algorithm applications is provided, and the main challenges and the corresponding solutions are analyzed. Finally, the importance of automated classification of marine fish images for related image information processing research is discussed, and its prospects in the field of marine monitoring are summarized.
Understanding the species composition, abundance and temporal and spatial distribution of fish on a global scale will help their biodiversity conservation. Underwater image acquisition is one of the main means to survey fish species diversity, but image data analysis is time-consuming and labor-intensive. Since 2015, a series of progress has been made in updating the datasets of marine fish images and optimizing the algorithm of deep learning models, but the performance of fine-grained classification is still insufficient, and the production practice application of research results is relatively weak. Therefore, the need for automated fish image classification in marine investigations is firstly studied. Then a comprehensive introduction to fish image datasets and deep learning algorithm applications is provided, and the main challenges and the corresponding solutions are analyzed. Finally, the importance of automated classification of marine fish images for related image information processing research is discussed, and its prospects in the field of marine monitoring are summarized.
Research on Symmetrically Resonant VLF Transmit/Receive Magnetoelectric Antenna Coupling Performance
Available online , doi: 10.11999/JEIT230247
Abstract:
Very low frequency has great potential for long distance signal transmission and military communications due to its low propagation loss characteristics. Magneto-Electric (ME) antennas, based on the acoustic resonance principle, can push the limits of size and are easily impedance matched, offering unique advantages for transmission in the very low frequency band. Based on this, a new ME antenna system consisting of a transmitting antenna of P/T/P structure and a receiving antenna of T/P/T structure is designed. The structural pattern of the antenna in receiving/transmitting electromagnetic waves is analyzed based on the magneto-mechanical coupling model. The magnetic field distribution of the antenna in the near-field range is investigated based on the radiation model. An experiment on the transmission/receiving of ME antennas in the very low frequency band is realized with acoustic wave mediated excitation. Experimentally obtained at resonant frequencies, the ME transmit/receive antenna is improved to 82.6% in output voltage and to 42.2% in communication range before the structure optimization compared to after the optimization when the piezoelectricity ratio is 0.66 and 0.34, respectively. Magnitude higher radiation efficiency is improved by three orders compared to the same size electric small antenna. Modulated communication with a transmission rate of 5 bit/s is possible and the performance of the antenna is improved based on structural optimization.
Very low frequency has great potential for long distance signal transmission and military communications due to its low propagation loss characteristics. Magneto-Electric (ME) antennas, based on the acoustic resonance principle, can push the limits of size and are easily impedance matched, offering unique advantages for transmission in the very low frequency band. Based on this, a new ME antenna system consisting of a transmitting antenna of P/T/P structure and a receiving antenna of T/P/T structure is designed. The structural pattern of the antenna in receiving/transmitting electromagnetic waves is analyzed based on the magneto-mechanical coupling model. The magnetic field distribution of the antenna in the near-field range is investigated based on the radiation model. An experiment on the transmission/receiving of ME antennas in the very low frequency band is realized with acoustic wave mediated excitation. Experimentally obtained at resonant frequencies, the ME transmit/receive antenna is improved to 82.6% in output voltage and to 42.2% in communication range before the structure optimization compared to after the optimization when the piezoelectricity ratio is 0.66 and 0.34, respectively. Magnitude higher radiation efficiency is improved by three orders compared to the same size electric small antenna. Modulated communication with a transmission rate of 5 bit/s is possible and the performance of the antenna is improved based on structural optimization.
Available online , doi: 10.11999/JEIT240070
Abstract:
In response to the challenges posed by the deformation of flexible polarized array antennas, which results in difficulties in beam reconstruction and compromised beam performance, polarization beam online reconstruction technique based on flexible deformation polarized antennas is proposed in this paper. Firstly, the deformation state of the array is modeled based on a wing model, and real-time deformation data is obtained using modal analysis to reconstruct the antenna array model online. Secondly, the element response of vector array antenna is utilized to construct a flexible array antenna signal model in three-dimensional space. Finally, a deep integration of the Cyclic Algorithm (CA) and Second-Order Cone Programming (SOCP) is employed to solve the dynamic optimization problem of this optimal polarization beam reconstruction. Simulation results demonstrate that within a certain range of deformation and under different arc and angle requirements from environmental loads, the proposed method can achieve online antenna array reconstruction and real-time optimal polarization beam reconstruction based on the dynamic antenna array model. The directional gain, beamwidth, and polarization matching design all meet the requirements for practical engineering applications.
In response to the challenges posed by the deformation of flexible polarized array antennas, which results in difficulties in beam reconstruction and compromised beam performance, polarization beam online reconstruction technique based on flexible deformation polarized antennas is proposed in this paper. Firstly, the deformation state of the array is modeled based on a wing model, and real-time deformation data is obtained using modal analysis to reconstruct the antenna array model online. Secondly, the element response of vector array antenna is utilized to construct a flexible array antenna signal model in three-dimensional space. Finally, a deep integration of the Cyclic Algorithm (CA) and Second-Order Cone Programming (SOCP) is employed to solve the dynamic optimization problem of this optimal polarization beam reconstruction. Simulation results demonstrate that within a certain range of deformation and under different arc and angle requirements from environmental loads, the proposed method can achieve online antenna array reconstruction and real-time optimal polarization beam reconstruction based on the dynamic antenna array model. The directional gain, beamwidth, and polarization matching design all meet the requirements for practical engineering applications.
Available online , doi: 10.11999/JEIT231219
Abstract:
Holographic Multiple-Input Multiple-Output (HMIMO) is an emerging technology for 6G communications. This type of array is composed of densely distributed antenna elements within a fixed aperture area. It is an extension of Massive MIMO technology under the practical constraints of antenna aperture. HMIMO systems have great potential in significantly improving wireless communication performance. However, due to the presence of closely spaced antennas, and the distane between antennas is less than half of the length, severe coupling effects are inevitable and traditional assumption of independent and identically distributed channel is invalid. Thus, designing an effective and practical channel model becomes one of the most challenging problems in HMIMO researches. To address these challenges, this paper investigates four channel modeling approaches based on electromagnetic field theory. The first approach is based on the plane Green’s function and models the integral of Green’s functions between planes with high complexity. The second and third approaches approximate the communication channel in HMIMO using plane wave expansion and spherical wave expansion, respectively, with lower complexity. The channel modeling based on plane wave expansion is relatively simple and is more suitable for far field, but would underestimate the maximum capacity of the system under strong coupling between antennas. The channel modeling based on spherical wave expansion better captures the characteristics of the electromagnetic wave channel but comes with higher complexity. Finally, a channel modeling method based on random Green’s functions is introduced, primarily describing the random characteristics of electromagnetic waves in rich scattering environments or Rayleigh channels.
Holographic Multiple-Input Multiple-Output (HMIMO) is an emerging technology for 6G communications. This type of array is composed of densely distributed antenna elements within a fixed aperture area. It is an extension of Massive MIMO technology under the practical constraints of antenna aperture. HMIMO systems have great potential in significantly improving wireless communication performance. However, due to the presence of closely spaced antennas, and the distane between antennas is less than half of the length, severe coupling effects are inevitable and traditional assumption of independent and identically distributed channel is invalid. Thus, designing an effective and practical channel model becomes one of the most challenging problems in HMIMO researches. To address these challenges, this paper investigates four channel modeling approaches based on electromagnetic field theory. The first approach is based on the plane Green’s function and models the integral of Green’s functions between planes with high complexity. The second and third approaches approximate the communication channel in HMIMO using plane wave expansion and spherical wave expansion, respectively, with lower complexity. The channel modeling based on plane wave expansion is relatively simple and is more suitable for far field, but would underestimate the maximum capacity of the system under strong coupling between antennas. The channel modeling based on spherical wave expansion better captures the characteristics of the electromagnetic wave channel but comes with higher complexity. Finally, a channel modeling method based on random Green’s functions is introduced, primarily describing the random characteristics of electromagnetic waves in rich scattering environments or Rayleigh channels.
Available online , doi: 10.11999/JEIT230887
Abstract:
Due to the complex marine environment, it is difficult for a maritime radar to achieve high-performance detection of slow and small targets on the sea surface. For such targets, the traditional energy-based statistical detection algorithms suffer from serious performance loss. Confronted with this problem, a detection algorithm of small targets based on Deep Graph Infomax framework is proposed to realize unsupervised target anomaly detection in the background of sea clutter. In the traditional neural networks, there is an assumption that the samples are independent and identically distributed, which, however, the high-resolution radar echo does not meet. Therefore, this paper re-models the data from the perspective of graph and constructs the graph topological structure according to the correlation characteristics of the echo. Moreover, this paper puts forward the relative maximum node degree, and combines it with the relative average amplitude and the relative Doppler vector entropy to be the initial representation vectors of the graph nodes. With the graph modeling done, the graph attention network is used as the encoder in the Deep Graph Infomax framework to learn representation vectors. Finally, the anomaly detection algorithm is used to detect the targets, and the false alarm can be controlled. The detection result on the measured datasets shows that the performance of the proposed detector is improved by 9.2% compared to the three-feature detector when using the fast convex hull learning algorithm. Compared to the time-frequency three-feature detector, the performance is improved by 7.9%. When the network outputs a higher-dimensional representation vectors, the performance of the detector using the isolated forest algorithm is improved by 27.4%.
Due to the complex marine environment, it is difficult for a maritime radar to achieve high-performance detection of slow and small targets on the sea surface. For such targets, the traditional energy-based statistical detection algorithms suffer from serious performance loss. Confronted with this problem, a detection algorithm of small targets based on Deep Graph Infomax framework is proposed to realize unsupervised target anomaly detection in the background of sea clutter. In the traditional neural networks, there is an assumption that the samples are independent and identically distributed, which, however, the high-resolution radar echo does not meet. Therefore, this paper re-models the data from the perspective of graph and constructs the graph topological structure according to the correlation characteristics of the echo. Moreover, this paper puts forward the relative maximum node degree, and combines it with the relative average amplitude and the relative Doppler vector entropy to be the initial representation vectors of the graph nodes. With the graph modeling done, the graph attention network is used as the encoder in the Deep Graph Infomax framework to learn representation vectors. Finally, the anomaly detection algorithm is used to detect the targets, and the false alarm can be controlled. The detection result on the measured datasets shows that the performance of the proposed detector is improved by 9.2% compared to the three-feature detector when using the fast convex hull learning algorithm. Compared to the time-frequency three-feature detector, the performance is improved by 7.9%. When the network outputs a higher-dimensional representation vectors, the performance of the detector using the isolated forest algorithm is improved by 27.4%.
Available online , doi: 10.11999/JEIT231330
Abstract:
For automatic recognition of the depression intensity in patients, the existing deep learning based methods typically face two main challenges: (1) It is difficult for deep models to effectively capture the global context information relevant to the level of depression intensity from facial expressions, and (2) the semantic consistency between the global semantic information and the local one associated with depression intensity is often ignored. One new deep neural network for recognizing the severity of depressive symptoms, by combining the Perceptually Locally-Enhanced Global Depression Features and the Fused Global-Local Semantic Correlation Features (PLEGDF-FGLSCF), is proposed in this paper. Firstly, the PLEGDF module for the extraction of global depression features with local perceptual enhancement, is designed to extract the semantic correlations among local facial regions, to promote the interactions between depression-relevant information in different local regions, and thus to enhance the expressiveness of the global depression features driven by the local ones. Secondly, in order to achieve full integration of global and local semantic features related to depression severity, the FGLSCF module is proposed, aiming to capture the correlation of global and local semantic information and thus to ensure the semantic consistency in describing the depression intensity by means of global and local semantic features. Finally, on the AVEC2013 and AVEC2014 datasets, the PLEGDF-FGLSCF model achieved recognition results in terms of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) with the values of 7.75/5.96 and 7.49/5.99, respectively, demonstrating its superiority to most existing benchmark methods, verifying the rationality and effectiveness of our approach.
For automatic recognition of the depression intensity in patients, the existing deep learning based methods typically face two main challenges: (1) It is difficult for deep models to effectively capture the global context information relevant to the level of depression intensity from facial expressions, and (2) the semantic consistency between the global semantic information and the local one associated with depression intensity is often ignored. One new deep neural network for recognizing the severity of depressive symptoms, by combining the Perceptually Locally-Enhanced Global Depression Features and the Fused Global-Local Semantic Correlation Features (PLEGDF-FGLSCF), is proposed in this paper. Firstly, the PLEGDF module for the extraction of global depression features with local perceptual enhancement, is designed to extract the semantic correlations among local facial regions, to promote the interactions between depression-relevant information in different local regions, and thus to enhance the expressiveness of the global depression features driven by the local ones. Secondly, in order to achieve full integration of global and local semantic features related to depression severity, the FGLSCF module is proposed, aiming to capture the correlation of global and local semantic information and thus to ensure the semantic consistency in describing the depression intensity by means of global and local semantic features. Finally, on the AVEC2013 and AVEC2014 datasets, the PLEGDF-FGLSCF model achieved recognition results in terms of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) with the values of 7.75/5.96 and 7.49/5.99, respectively, demonstrating its superiority to most existing benchmark methods, verifying the rationality and effectiveness of our approach.
Available online , doi: 10.11999/JEIT231249
Abstract:
Radar possesses the capability for all-day, all-weather observation and can generate radar target images through image processing. It serves as an indispensable piece of remote sensing equipment in various civil and military applications, including earth observation, and surveillance. High-resolution radar images can provide a detailed outline and fine structure of the target, which is conducive to subsequent applications such as target classification and recognition. For the acquired radar images, how to use theoretical methods such as signal and information processing to further improve the resolution and break through the Rayleigh limit has important scientific research and practical application value. On the other hand, polarization, a crucial attribute of electromagnetic waves, plays a significant role in the acquisition and analysis of target characteristics, and can provide rich information for super-resolution reconstruction. Accordingly, this work initially elucidates the concept of polarimetric radar image super-resolution reconstruction, summarizes the performance evaluation metrics, and primarily focuses on the methods of polarimetric radar image super-resolution reconstruction and their applications. Lastly, the limitations of existing methods are summarized and potential future trends in technology are forecasted.
Radar possesses the capability for all-day, all-weather observation and can generate radar target images through image processing. It serves as an indispensable piece of remote sensing equipment in various civil and military applications, including earth observation, and surveillance. High-resolution radar images can provide a detailed outline and fine structure of the target, which is conducive to subsequent applications such as target classification and recognition. For the acquired radar images, how to use theoretical methods such as signal and information processing to further improve the resolution and break through the Rayleigh limit has important scientific research and practical application value. On the other hand, polarization, a crucial attribute of electromagnetic waves, plays a significant role in the acquisition and analysis of target characteristics, and can provide rich information for super-resolution reconstruction. Accordingly, this work initially elucidates the concept of polarimetric radar image super-resolution reconstruction, summarizes the performance evaluation metrics, and primarily focuses on the methods of polarimetric radar image super-resolution reconstruction and their applications. Lastly, the limitations of existing methods are summarized and potential future trends in technology are forecasted.
Available online , doi: 10.11999/JEIT231234
Abstract:
In vertical federated learning, the datasets of the clients have overlapping sample IDs and features of different dimensions, thus the data alignment is necessary for model training. As the intersection of the sample IDs is public in current data alignment technologies, how to align the data without any leakage of the intersection becomes a key issue. The proposing private-preserving data alignment framework is based on interchangeable encryption and homomorphic encryption technologies, mainly including data encryption, ciphertext blinding, private intersecting, and feature splicing. The sample IDs are encrypted twice based on an interchangeable encryption algorithm, where the same ciphertexts correspond to the same plaintexts, and the sample features are encrypted and then randomly blinded based on a homomorphic encryption algorithm. The intersection of the encrypted sample IDs is obtained, and the corresponding features are then spliced and secretly shared with the participants. Compared to the existing technologies, the privacy of the ID intersection is protected, and the samples corresponding to the IDs outside intersection can be removed safely in our framework. The security proof shows that each participant cannot obtain any knowledge of each other except for the data size, which guarantees the effectiveness of the private-preserving strategies. The simulation experiments demonstrate that the runtime is shortened about 1.3 seconds and the model accuracy keeps higher than 85% with every 10% reduction of the redundant data. The simulation experimental results show that using the ALIGN framework for vertical federated learning data alignment is beneficial for improving the efficiency and accuracy of subsequent model training.
In vertical federated learning, the datasets of the clients have overlapping sample IDs and features of different dimensions, thus the data alignment is necessary for model training. As the intersection of the sample IDs is public in current data alignment technologies, how to align the data without any leakage of the intersection becomes a key issue. The proposing private-preserving data alignment framework is based on interchangeable encryption and homomorphic encryption technologies, mainly including data encryption, ciphertext blinding, private intersecting, and feature splicing. The sample IDs are encrypted twice based on an interchangeable encryption algorithm, where the same ciphertexts correspond to the same plaintexts, and the sample features are encrypted and then randomly blinded based on a homomorphic encryption algorithm. The intersection of the encrypted sample IDs is obtained, and the corresponding features are then spliced and secretly shared with the participants. Compared to the existing technologies, the privacy of the ID intersection is protected, and the samples corresponding to the IDs outside intersection can be removed safely in our framework. The security proof shows that each participant cannot obtain any knowledge of each other except for the data size, which guarantees the effectiveness of the private-preserving strategies. The simulation experiments demonstrate that the runtime is shortened about 1.3 seconds and the model accuracy keeps higher than 85% with every 10% reduction of the redundant data. The simulation experimental results show that using the ALIGN framework for vertical federated learning data alignment is beneficial for improving the efficiency and accuracy of subsequent model training.
Available online , doi: 10.11999/JEIT231161
Abstract:
Currently, the Contrastive Language-Image Pre-training (CLIP) has shown great potential in zero-shot 3D shape classification. However, there is a large modality gap between 3D shapes and texts, which limits further improvement of classification accuracy. To address the problem, a zero-shot 3D shape classification method based on semantic-enhanced CLIP is proposed in this paper. Firstly, 3D shapes are represented as views. Then, in order to improve recognition ability of unknown categories in zero-shot learning, the semantic descriptive text of each view and its corresponding category are obtained through a visual language generative model, and it is used as the semantic bridge between views and category prompt texts. The semantic descriptive texts are obtained through image captioning and visual question answering. Finally, the finely-adjusted semantic encoder is used to concretize the semantic descriptive texts to the semantic descriptions of each category, which have rich semantic information and strong interpretability, and effectively reduce the semantic gap between views and category prompt texts. Experiments show that our method outperforms existing zero-shot classification methods on the ModelNet10 and ModelNet40 datasets.
Currently, the Contrastive Language-Image Pre-training (CLIP) has shown great potential in zero-shot 3D shape classification. However, there is a large modality gap between 3D shapes and texts, which limits further improvement of classification accuracy. To address the problem, a zero-shot 3D shape classification method based on semantic-enhanced CLIP is proposed in this paper. Firstly, 3D shapes are represented as views. Then, in order to improve recognition ability of unknown categories in zero-shot learning, the semantic descriptive text of each view and its corresponding category are obtained through a visual language generative model, and it is used as the semantic bridge between views and category prompt texts. The semantic descriptive texts are obtained through image captioning and visual question answering. Finally, the finely-adjusted semantic encoder is used to concretize the semantic descriptive texts to the semantic descriptions of each category, which have rich semantic information and strong interpretability, and effectively reduce the semantic gap between views and category prompt texts. Experiments show that our method outperforms existing zero-shot classification methods on the ModelNet10 and ModelNet40 datasets.
Available online , doi: 10.11999/JEIT231074
Abstract:
In order to solve the problem that the travel information of vehicle users in Intelligent Transportation System (ITS) is easy to be illegally stolen and the traffic data stored in the cloud server of transportation system is abused by malicious users, a new Attribute Based Searchable Encryption (ABSE) scheme is proposed in this paper, which has the functions of privacy protection, key aggregation and lightweight calculation. The scheme realizes full privacy protection in key generation stage, access control stage and partial decryption stage. The search keyword is embedded into the access structure, which can realize partial policy hiding and keyword security. Through key aggregation technology, all file identities that meet the search conditions and access policies are aggregated into one aggregate key, which reduces the burden of key storage for users, and further ensures the security of file keys and data. The security analysis shows that the scheme has the advantages of hidden access structure security, keyword ciphertext indistinguishable security and trapdoor indistinguishable security. The theoretical analysis and numerical simulation showed the proposed scheme was efficient and practical in terms of communication and computing overhead.
In order to solve the problem that the travel information of vehicle users in Intelligent Transportation System (ITS) is easy to be illegally stolen and the traffic data stored in the cloud server of transportation system is abused by malicious users, a new Attribute Based Searchable Encryption (ABSE) scheme is proposed in this paper, which has the functions of privacy protection, key aggregation and lightweight calculation. The scheme realizes full privacy protection in key generation stage, access control stage and partial decryption stage. The search keyword is embedded into the access structure, which can realize partial policy hiding and keyword security. Through key aggregation technology, all file identities that meet the search conditions and access policies are aggregated into one aggregate key, which reduces the burden of key storage for users, and further ensures the security of file keys and data. The security analysis shows that the scheme has the advantages of hidden access structure security, keyword ciphertext indistinguishable security and trapdoor indistinguishable security. The theoretical analysis and numerical simulation showed the proposed scheme was efficient and practical in terms of communication and computing overhead.
Available online , doi: 10.11999/JEIT231457
Abstract:
Logic synthesis plays a crucial role in the modern electronic design automation process. With the continuous enhancement of computational capabilities and the emergence of new computing paradigms, various efficient Boolean SATisfiability (SAT) solvers and circuit simulators have been developed and applied in the context of logic synthesis. First, the overview of the Boolean Satisfiability problem and circuit logic simulator is briefly described. Subsequently, the historical development of the matrix semi-tensor product is reviewed, and based on the fundamental principles of the semi-tensor product, its research progress in inference engines and logic synthesis is expounded. Finally, a prospective analysis is conducted on emerging technologies that may significantly impact logic synthesis in the future.
Logic synthesis plays a crucial role in the modern electronic design automation process. With the continuous enhancement of computational capabilities and the emergence of new computing paradigms, various efficient Boolean SATisfiability (SAT) solvers and circuit simulators have been developed and applied in the context of logic synthesis. First, the overview of the Boolean Satisfiability problem and circuit logic simulator is briefly described. Subsequently, the historical development of the matrix semi-tensor product is reviewed, and based on the fundamental principles of the semi-tensor product, its research progress in inference engines and logic synthesis is expounded. Finally, a prospective analysis is conducted on emerging technologies that may significantly impact logic synthesis in the future.
Available online , doi: 10.11999/JEIT231367
Abstract:
A sparse Bayesian estimation for spatial Radio Frequency Interference (RFI) of synthetic aperture microwave radiometers is proposed in this paper. Firstly, an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established. The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view. Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution, the transformation coefficients of brightness temperature in the support domain are sparse. Under the Sparse Bayesian Learning (SBL) framework, brightness temperature is sparsely reconstructed. Notably, this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters. The effectiveness of this method is verified through computer simulations.
A sparse Bayesian estimation for spatial Radio Frequency Interference (RFI) of synthetic aperture microwave radiometers is proposed in this paper. Firstly, an interferometry measurement model of the visibility function for synthetic aperture microwave radiometers is established. The observed data are expressed as the product of the observation matrix of the aperture synthesis antenna baseline correlation steering vector and the brightness temperature of the field of view. Due to the orthogonality of the observation matrix and the sparsity of the RFI spatial angle distribution, the transformation coefficients of brightness temperature in the support domain are sparse. Under the Sparse Bayesian Learning (SBL) framework, brightness temperature is sparsely reconstructed. Notably, this method can obtain high reconstruction performance without the prior information of sparsity and regularization parameters. The effectiveness of this method is verified through computer simulations.
Available online , doi: 10.11999/JEIT231423
Abstract:
In response to the challenge of ensuring positioning accuracy in environments where the Global Navigation Satellite System (GNSS) is denied, a positioning scheme based on opportunistic New Radio (NR) signals is devised and an Interference Cancellation Subspace Pursuit (ICSP) algorithm is proposed in this paper. This algorithm aims to resolve the issue of inadequate precision in the extraction of positioning observations due to co-channel interference within Ultra-Dense Networks (UDNs) and Heterogeneous Networks (HetNets). The effectiveness of the ICSP algorithm in optimizing the performance of 5G opportunistic signal receivers and enhancing positioning accuracy in complex network environments has been validated through simulation experiments and semi-physical simulations utilizing the Universal Software Radio Peripheral (USRP).
In response to the challenge of ensuring positioning accuracy in environments where the Global Navigation Satellite System (GNSS) is denied, a positioning scheme based on opportunistic New Radio (NR) signals is devised and an Interference Cancellation Subspace Pursuit (ICSP) algorithm is proposed in this paper. This algorithm aims to resolve the issue of inadequate precision in the extraction of positioning observations due to co-channel interference within Ultra-Dense Networks (UDNs) and Heterogeneous Networks (HetNets). The effectiveness of the ICSP algorithm in optimizing the performance of 5G opportunistic signal receivers and enhancing positioning accuracy in complex network environments has been validated through simulation experiments and semi-physical simulations utilizing the Universal Software Radio Peripheral (USRP).
Available online , doi: 10.11999/JEIT230878
Abstract:
As a result of the characteristics of UnderWater Acoustic (UWA) channel, such as rapid fading of Channel Frequency Response (CFR) due to large delay spreading, the development of UnderWater Acoustic Communication (UWAC) technology is challenged. The acquisition of effective and reliable Channel State Information (CSI) at the transmitter is a prerequisite for adaptive communication. To meet the needs of UWA adaptive OFDM communication, a CSI-Grouping-Sequencing-Fitting-Feedback (CSI-GSFF) based on sequenced codebook algorithm is proposed, which consists of three steps, including grouping, sequencing, and data fitting. Firstly, adjacent pilot subcarriers are divided into several groups and each group is seen as a feedback cell. Then, the pilot subcarriers within each group are sorted according to the channel gains to mitigate adverse effects such as high feedback overhead caused by the rapid fading of CFR. Finally, polynomial fitting is performed, and the sorting operation effectively reduces the fitting order. Through the simulation of time-varying channel data in sea trials, the results show that the CSI-GSFF algorithm can achieve the Bit Error Rate (BER) performance of the UWA adaptive OFDM communication system under the perfect CSI, while the CSI-GSFF algorithm can effectively reduce the feedback overhead.
As a result of the characteristics of UnderWater Acoustic (UWA) channel, such as rapid fading of Channel Frequency Response (CFR) due to large delay spreading, the development of UnderWater Acoustic Communication (UWAC) technology is challenged. The acquisition of effective and reliable Channel State Information (CSI) at the transmitter is a prerequisite for adaptive communication. To meet the needs of UWA adaptive OFDM communication, a CSI-Grouping-Sequencing-Fitting-Feedback (CSI-GSFF) based on sequenced codebook algorithm is proposed, which consists of three steps, including grouping, sequencing, and data fitting. Firstly, adjacent pilot subcarriers are divided into several groups and each group is seen as a feedback cell. Then, the pilot subcarriers within each group are sorted according to the channel gains to mitigate adverse effects such as high feedback overhead caused by the rapid fading of CFR. Finally, polynomial fitting is performed, and the sorting operation effectively reduces the fitting order. Through the simulation of time-varying channel data in sea trials, the results show that the CSI-GSFF algorithm can achieve the Bit Error Rate (BER) performance of the UWA adaptive OFDM communication system under the perfect CSI, while the CSI-GSFF algorithm can effectively reduce the feedback overhead.
Available online , doi: 10.11999/JEIT230953
Abstract:
With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
With the growing use of smart devices, the ease of sharing digital media content has been enhanced. Concerns have been raised about unauthorized access, particularly via screen shooting. In this paper, a novel end-to-end watermarking framework is proposed, employing invertible neural networks and inverse gradient attention, to tackle the copyright infringement challenges related to screen content leakage. A single invertible neural network is employed by the proposed method for watermark embedding and extraction, ensuring information integrity during network propagation. Additionally, robustness and visual quality are enhanced by an inverse gradient attention module, which emphasizes pixel values and embeds the watermark in imperceptible areas for better invisibility and model resilience. Model parameters are optimized using the Learnable Perceptual Image Patch Similarity (LPIPS) loss function, minimizing perception differences in watermarked images. The superiority of this approach over existing learning-based screen-shooting resilient watermarking methods in terms of robustness and visual quality is demonstrated by experimental results.
Available online , doi: 10.11999/JEIT231199
Abstract:
Unmanned Aerial Vehicles (UAV) technology holds significant importance and offers extensive potential for application in the field of inspection. Taking into account the limited endurance of the UAV, it needs to fly from the nest to the designated inspection area, complete the inspection of the transmission tower, and then return to the nest safely before the battery is exhausted. For large-scale inspection scenarios aiming, a multi-UAV inspection method is proposed to minimize inspection time. In detail, the k-means++ algorithm is used to optimize UAVs task allocation and the modified simulated annealing algorithm is utilized to optimize the inspection trajectory to improve inspection efficiency. Finally, the tower pole distribution data from a simulated real-world environment, the proposed algorithm is employed to assign UAVs tasks and design trajectories. The simulation results confirm that the proposed algorithm can significantly reduce the total inspection time through multi-UAV task allocation and trajectory design.
Unmanned Aerial Vehicles (UAV) technology holds significant importance and offers extensive potential for application in the field of inspection. Taking into account the limited endurance of the UAV, it needs to fly from the nest to the designated inspection area, complete the inspection of the transmission tower, and then return to the nest safely before the battery is exhausted. For large-scale inspection scenarios aiming, a multi-UAV inspection method is proposed to minimize inspection time. In detail, the k-means++ algorithm is used to optimize UAVs task allocation and the modified simulated annealing algorithm is utilized to optimize the inspection trajectory to improve inspection efficiency. Finally, the tower pole distribution data from a simulated real-world environment, the proposed algorithm is employed to assign UAVs tasks and design trajectories. The simulation results confirm that the proposed algorithm can significantly reduce the total inspection time through multi-UAV task allocation and trajectory design.
Available online , doi: 10.11999/JEIT230705
Abstract:
Spiking Neural Networks (SNN) have a signal processing mode close to the cerebral cortex, which is considered to be an important approach to realize brain-inspired computing. However, the lack of effective supervised learning algorithms for deep spiking neural networks has been a great challenge for spiking sequence label-based brain-inspired computing tasks. A supervised learning algorithm for training deep spiking neural network is proposed in this paper. It is an error backpropagation algorithm that uses surrogate gradient to solve the problem of non-differentiable spike generation function, and define the postsynaptic potential and membrane potential reversal iteration factors represent the spatial and temporal dependencies of pulsed neurons, respectively. It differs from existing learning algorithms based on firing rate encoding, fully reflects analytically the temporal dynamic properties of the spiking neuron. Therefore, the algorithm proposed in this paper is well-suited for application to tasks that require longer time sequences rather than spiking firing rates, such as behavior control. Proposed algorithm is validated on the static image datasets CIFAR10, and the neuromorphic dataset NMNIST. It shows good performance on all these datasets, which helps to further investigate spike-based brain-inspired computation.
Spiking Neural Networks (SNN) have a signal processing mode close to the cerebral cortex, which is considered to be an important approach to realize brain-inspired computing. However, the lack of effective supervised learning algorithms for deep spiking neural networks has been a great challenge for spiking sequence label-based brain-inspired computing tasks. A supervised learning algorithm for training deep spiking neural network is proposed in this paper. It is an error backpropagation algorithm that uses surrogate gradient to solve the problem of non-differentiable spike generation function, and define the postsynaptic potential and membrane potential reversal iteration factors represent the spatial and temporal dependencies of pulsed neurons, respectively. It differs from existing learning algorithms based on firing rate encoding, fully reflects analytically the temporal dynamic properties of the spiking neuron. Therefore, the algorithm proposed in this paper is well-suited for application to tasks that require longer time sequences rather than spiking firing rates, such as behavior control. Proposed algorithm is validated on the static image datasets CIFAR10, and the neuromorphic dataset NMNIST. It shows good performance on all these datasets, which helps to further investigate spike-based brain-inspired computation.
Available online , doi: 10.11999/JEIT230551
Abstract:
In a human-centered smart factory, perceiving and understanding workers’ behavior is crucial, as different job categories are often associated with work time and tasks. In this paper, the accuracy of the model's recognition is improved by combining two approaches, namely adaptive graphs and Transformers, to focus more on the spatiotemporal information of the skeletal structure. Firstly, an adaptive graph method is employed to capture the connectivity relationships beyond the human body skeleton. Furthermore, the Transformer framework is utilized to capture the dynamic temporal variations of the worker's skeleton. To evaluate the model's performance, six typical worker action datasets are created for intelligent production line assembly tasks and validated. The results indicate that the model proposed in this article has a Top-1 accuracy comparable to mainstream action recognition models. Finally, the proposed model is compared with several mainstream methods on the publicly available NTU-RGBD and Skeleton-Kinetics datasets, and the experimental results demonstrate the robustness of the model proposed in this paper.
In a human-centered smart factory, perceiving and understanding workers’ behavior is crucial, as different job categories are often associated with work time and tasks. In this paper, the accuracy of the model's recognition is improved by combining two approaches, namely adaptive graphs and Transformers, to focus more on the spatiotemporal information of the skeletal structure. Firstly, an adaptive graph method is employed to capture the connectivity relationships beyond the human body skeleton. Furthermore, the Transformer framework is utilized to capture the dynamic temporal variations of the worker's skeleton. To evaluate the model's performance, six typical worker action datasets are created for intelligent production line assembly tasks and validated. The results indicate that the model proposed in this article has a Top-1 accuracy comparable to mainstream action recognition models. Finally, the proposed model is compared with several mainstream methods on the publicly available NTU-RGBD and Skeleton-Kinetics datasets, and the experimental results demonstrate the robustness of the model proposed in this paper.
Available online , doi: 10.11999/JEIT231374
Abstract:
Addressing the issues of inadequate performance in constructing Radio Environment Maps (REMs) in complex scenarios due to non-penetrable obstacles for electromagnetic waves, and the arbitrary selection of interpolation neighborhoods imposed by Inverse Distance Weighted (IDW), a Voronoi-based Inverse Obstacle Distance Weighted algorithm (VIODW) is proposed in this paper. This algorithm adaptively defines interpolation neighborhoods for each interpolation point by creating Voronoi diagrams incorporating obstacles for numerical computation. Then, using the ANY-Angle (ANYA) Algorithm to calculate the obstacle distance between the interpolation point and each monitoring station within the interpolation neighborhood. Finally, by calculating the weighted mean with the inverse power of the obstacle distance as the weight, the value at the point is obtained, achieving high-precision construction of REMs in complex scenarios. Both theoretical analysis and simulation results demonstrate that this method offers excellent construction accuracy and can accurately model the power distribution of electromagnetic waves in complex scenarios. Hence, it provides an effective approach for high-precision REM construction in complex scenarios.
Addressing the issues of inadequate performance in constructing Radio Environment Maps (REMs) in complex scenarios due to non-penetrable obstacles for electromagnetic waves, and the arbitrary selection of interpolation neighborhoods imposed by Inverse Distance Weighted (IDW), a Voronoi-based Inverse Obstacle Distance Weighted algorithm (VIODW) is proposed in this paper. This algorithm adaptively defines interpolation neighborhoods for each interpolation point by creating Voronoi diagrams incorporating obstacles for numerical computation. Then, using the ANY-Angle (ANYA) Algorithm to calculate the obstacle distance between the interpolation point and each monitoring station within the interpolation neighborhood. Finally, by calculating the weighted mean with the inverse power of the obstacle distance as the weight, the value at the point is obtained, achieving high-precision construction of REMs in complex scenarios. Both theoretical analysis and simulation results demonstrate that this method offers excellent construction accuracy and can accurately model the power distribution of electromagnetic waves in complex scenarios. Hence, it provides an effective approach for high-precision REM construction in complex scenarios.
Available online , doi: 10.11999/JEIT231186
Abstract:
A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access (NOMA) systems, which can avoid the required expensive signaling overhead in successive interference cancellation algorithms, especially for NOMA-based short packet transmission. Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network, the original convolutional network is replaced by the adder network. The modulation detection accuracy, computing delay and energy consumption are fully compared for two kinds of network architectures. Meanwhile, time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio. Finally, the influence of power allocation and data packet length on detection performance is analyzed and verified.
A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access (NOMA) systems, which can avoid the required expensive signaling overhead in successive interference cancellation algorithms, especially for NOMA-based short packet transmission. Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network, the original convolutional network is replaced by the adder network. The modulation detection accuracy, computing delay and energy consumption are fully compared for two kinds of network architectures. Meanwhile, time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio. Finally, the influence of power allocation and data packet length on detection performance is analyzed and verified.
Available online , doi: 10.11999/JEIT230679
Abstract:
Anomaly detection is an important task to maintain cloud data center performance. A large number of cloud servers are running in cloud data centers to implement various cloud computing functions. Since the performance of cloud data centers depends on the normal operation of cloud services, it is crucial to detect and analyze anomalies in cloud servers. To this end, a cloud server anomaly detection model based on time series decomposition and spatiotemporal information extraction-Multi-Channel Bidirectional Wasserstein Generative Adversarial Networks with Graph-Time Network (MCBiWGAN-GTN) is proposed in this paper. Firstly, the Bidirectional Wasserstein GAN with Graph-Time Network (BiWGAN-GTN) algorithm is proposed. This algorithm is built upon the Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) algorithm. In this modification, the generator and encoder are replaced by a spatiotemporal information extraction module— Graph-Time Network (GTN) composed of Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN). This modification aims to extract spatiotemporal information from the data, enhancing the capabilities of the algorithm. Secondly, the semi-supervised BiWGAN-GTN algorithm is proposed to identify anomalies in multi-dimensional time series to avoid the risk of abnormal data intrusion during the training process and enhance model robustness. Finally, the MCBiWGAN-GTN is designed to achieve the goal of reducing data complexity and improving model learning efficiency. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (CEEMDAN) is used to decompose the time series data, and then different components are sent to the BiWGAN-GTN algorithm under the corresponding channel for training. The effectiveness and stability of the proposed model are verified on two real-world cloud data center datasets, Clearwater and MBD, using three evaluation metrics: precision, recall and F1 score. Experimental results show that the performance of MCBiWGAN-GTN on these two datasets is stable and better than the compared methods.
Anomaly detection is an important task to maintain cloud data center performance. A large number of cloud servers are running in cloud data centers to implement various cloud computing functions. Since the performance of cloud data centers depends on the normal operation of cloud services, it is crucial to detect and analyze anomalies in cloud servers. To this end, a cloud server anomaly detection model based on time series decomposition and spatiotemporal information extraction-Multi-Channel Bidirectional Wasserstein Generative Adversarial Networks with Graph-Time Network (MCBiWGAN-GTN) is proposed in this paper. Firstly, the Bidirectional Wasserstein GAN with Graph-Time Network (BiWGAN-GTN) algorithm is proposed. This algorithm is built upon the Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) algorithm. In this modification, the generator and encoder are replaced by a spatiotemporal information extraction module— Graph-Time Network (GTN) composed of Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN). This modification aims to extract spatiotemporal information from the data, enhancing the capabilities of the algorithm. Secondly, the semi-supervised BiWGAN-GTN algorithm is proposed to identify anomalies in multi-dimensional time series to avoid the risk of abnormal data intrusion during the training process and enhance model robustness. Finally, the MCBiWGAN-GTN is designed to achieve the goal of reducing data complexity and improving model learning efficiency. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm (CEEMDAN) is used to decompose the time series data, and then different components are sent to the BiWGAN-GTN algorithm under the corresponding channel for training. The effectiveness and stability of the proposed model are verified on two real-world cloud data center datasets, Clearwater and MBD, using three evaluation metrics: precision, recall and F1 score. Experimental results show that the performance of MCBiWGAN-GTN on these two datasets is stable and better than the compared methods.
Available online , doi: 10.11999/JEIT230672
Abstract:
In order to address the low spectral efficiency and inefficient link utilization problem in cooperative relay communication system, a Backscatter-NOMA enabled hybrid multicast-unicast cooperative transmission scheme is proposed for the scenario of coexistence of multicast and unicast services. A multicast user is opportunistically selected as a cooperative node, which used a part of the power of the received signal for its own decoding, and backscatter the residual power to enhance the reception quality of other users. To improve system performance, the minimum achievable rate for unicast users is maximized by jointly optimizing the base station power allocation coefficients, cooperative user backscatter coefficient and cooperative node selection variable, while guaranteeing the quality of service for multicast. To solve the above highly non-convex joint optimization problem, a cooperative user selection criterion was designed and an iterative algorithm was proposed to obtain the optimal solution to the original problem. The simulation results verify the fast convergence of the proposed iterative algorithm, which can improve the minimum achievable rate of unicast users by 11.5% compared to the non-cooperative transmission scheme, and effectively ensure the quality of multi-service.
In order to address the low spectral efficiency and inefficient link utilization problem in cooperative relay communication system, a Backscatter-NOMA enabled hybrid multicast-unicast cooperative transmission scheme is proposed for the scenario of coexistence of multicast and unicast services. A multicast user is opportunistically selected as a cooperative node, which used a part of the power of the received signal for its own decoding, and backscatter the residual power to enhance the reception quality of other users. To improve system performance, the minimum achievable rate for unicast users is maximized by jointly optimizing the base station power allocation coefficients, cooperative user backscatter coefficient and cooperative node selection variable, while guaranteeing the quality of service for multicast. To solve the above highly non-convex joint optimization problem, a cooperative user selection criterion was designed and an iterative algorithm was proposed to obtain the optimal solution to the original problem. The simulation results verify the fast convergence of the proposed iterative algorithm, which can improve the minimum achievable rate of unicast users by 11.5% compared to the non-cooperative transmission scheme, and effectively ensure the quality of multi-service.
Available online , doi: 10.11999/JEIT231283
Abstract:
To better leverage complementary image information from infrared and visible light images and generate fused images that align with human perception characteristics, a two-stage training strategy is proposed to obtain a novel infrared-visible image fusion Network based on pre-trained fixed Parameters and Deep feature modulation (PDNet). Specifically, in the self-supervised pre-training stage, a substantial dataset of clear natural images is employed as both inputs and outputs for the UNet backbone network, and pre-training is accomplished with autoencoder technology. As such, the resulting encoder module can proficiently extract multi-scale depth features from the input image, while the decoder module can faithfully reconstruct it into an output image with minimal deviation from the input. In the unsupervised fusion training stage, the pre-trained encoder and decoder module parameters remain fixed, and a fusion module featuring a Transformer structure is introduced between them. Within the Transformer structure, the multi-head self-attention mechanism allocates deep feature weights, extracted by the encoder from both infrared and visible light images, in a rational manner. This process fuses and modulates the deep image features at various scales into the manifold space of deep features of clear natural image, thereby ensuring the visual perception quality of the fused image after reconstruction by the decoder. Extensive experimental results demonstrate that, in comparison to current mainstream fusion models (algorithms), the proposed PDNet model exhibits substantial advantages across various objective evaluation metrics. Furthermore, in subjective visual evaluations, it aligns more closely with human visual perception characteristics.
To better leverage complementary image information from infrared and visible light images and generate fused images that align with human perception characteristics, a two-stage training strategy is proposed to obtain a novel infrared-visible image fusion Network based on pre-trained fixed Parameters and Deep feature modulation (PDNet). Specifically, in the self-supervised pre-training stage, a substantial dataset of clear natural images is employed as both inputs and outputs for the UNet backbone network, and pre-training is accomplished with autoencoder technology. As such, the resulting encoder module can proficiently extract multi-scale depth features from the input image, while the decoder module can faithfully reconstruct it into an output image with minimal deviation from the input. In the unsupervised fusion training stage, the pre-trained encoder and decoder module parameters remain fixed, and a fusion module featuring a Transformer structure is introduced between them. Within the Transformer structure, the multi-head self-attention mechanism allocates deep feature weights, extracted by the encoder from both infrared and visible light images, in a rational manner. This process fuses and modulates the deep image features at various scales into the manifold space of deep features of clear natural image, thereby ensuring the visual perception quality of the fused image after reconstruction by the decoder. Extensive experimental results demonstrate that, in comparison to current mainstream fusion models (algorithms), the proposed PDNet model exhibits substantial advantages across various objective evaluation metrics. Furthermore, in subjective visual evaluations, it aligns more closely with human visual perception characteristics.
Available online , doi: 10.11999/JEIT231223
Abstract:
Compared with traditional Two-Dimensional (2D) Synthetic Aperture Radar (SAR) imaging, Three-Dimensional (3D) SAR imaging technology can overcome problems such as overlay and geometric distortion, thus having broad development space. As a typical 3D imaging system, the elevation resolution of array SAR is generally limited by the array aperture in theory, which is much lower than the range and azimuth resolution. To address this issue, an assumption of consistency in elevation between neighboring pixels is introduced and a re-weighted locally joint sparsity based Compressed Sensing (CS) approach is proposed for the array super-resolution imaging in the height dimension. Then, typical clustering methods such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to achieve clustering analysis of specific targets (such as buildings and vehicles) in the observation scene. Finally, the experimental analysis using measured data is performed to confirm the effectiveness of the proposed algorithm.
Compared with traditional Two-Dimensional (2D) Synthetic Aperture Radar (SAR) imaging, Three-Dimensional (3D) SAR imaging technology can overcome problems such as overlay and geometric distortion, thus having broad development space. As a typical 3D imaging system, the elevation resolution of array SAR is generally limited by the array aperture in theory, which is much lower than the range and azimuth resolution. To address this issue, an assumption of consistency in elevation between neighboring pixels is introduced and a re-weighted locally joint sparsity based Compressed Sensing (CS) approach is proposed for the array super-resolution imaging in the height dimension. Then, typical clustering methods such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to achieve clustering analysis of specific targets (such as buildings and vehicles) in the observation scene. Finally, the experimental analysis using measured data is performed to confirm the effectiveness of the proposed algorithm.
Available online , doi: 10.11999/JEIT240051
Abstract:
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
Available online , doi: 10.11999/JEIT231460
Abstract:
The high-quality generation and precise phase synchronization of high-speed modulated baseband signals are key technologies of space optical communication ranging system. Traditional approaches relying on FPGA or Digital Signal Processor (DSP) and high-speed Digital to Analog Convertor (DAC) technology often suffer from limited phase synchronization accuracy and high hardware complexity. A method for high-speed optical communication baseband signal generation and phase synchronization is proposed and a phase-locked dynamic control loop is designed in this paper. By dynamically adjusting the phase of the high-speed signal transmission clock in real time, the deterministic relationship between the I/Q high-speed baseband signal phase and the external reference clock phase can be achieved. The experimental results demonstrate impressive performance metrics: When the code rate is of the Quadrature Phase Shift Keying (QPSK) optical modulated signal is 2.5 Gbit/s, the phase synchronization accuracy is less than 2 ps and the Error Vector Magnitude (EVM) is less than 8%; the bit error rate is 10–7 at a 5 Gbit/s optical communication rate, the receiver sensitivity is better than –47 dBm, and the ranging accuracy is better than 2 mm. Compared with traditional methods, both sensitivity and ranging accuracy are significantly improved.
The high-quality generation and precise phase synchronization of high-speed modulated baseband signals are key technologies of space optical communication ranging system. Traditional approaches relying on FPGA or Digital Signal Processor (DSP) and high-speed Digital to Analog Convertor (DAC) technology often suffer from limited phase synchronization accuracy and high hardware complexity. A method for high-speed optical communication baseband signal generation and phase synchronization is proposed and a phase-locked dynamic control loop is designed in this paper. By dynamically adjusting the phase of the high-speed signal transmission clock in real time, the deterministic relationship between the I/Q high-speed baseband signal phase and the external reference clock phase can be achieved. The experimental results demonstrate impressive performance metrics: When the code rate is of the Quadrature Phase Shift Keying (QPSK) optical modulated signal is 2.5 Gbit/s, the phase synchronization accuracy is less than 2 ps and the Error Vector Magnitude (EVM) is less than 8%; the bit error rate is 10–7 at a 5 Gbit/s optical communication rate, the receiver sensitivity is better than –47 dBm, and the ranging accuracy is better than 2 mm. Compared with traditional methods, both sensitivity and ranging accuracy are significantly improved.
Available online , doi: 10.11999/JEIT231232
Abstract:
Narrowband radar is widely used in the field of air defense guidance due to its advantages of low cost and long operating range. With the development of high-speed mobile platforms, traditional target recognition methods based on feature modeling of long-term observation echo sequences are no longer applicable. In response to the problem of poor feature recognition ability of narrowband radar for Observe Echoes for a Short period of Time (OEST) sequences and susceptibility to bait target interference, resulting in low reliability of recognition results, a narrowband radar OEST sequence air target recognition method using multi feature adaptive fusion is proposed in this paper. Firstly, the encoder and classification layers are constructed with channel-spatial attention modules and trained to adaptively enhance features with high separability. Then, the maximum edge orthogonal loss function is proposed to increase the feature spacing between different classes, reduce the feature spacing between the same classes, and make the feature vectors orthogonal between different classes; Finally, the parameters of the encoder layer and classification layer are fixed, and the decoder layer is trained using reconstruction loss value to ensure that the model has accurate identification ability for decoy targets. Under the condition of an observation sequence length of 100, the classification accuracy and discrimination rate of the experimental part reached 94.37% and 96.78%, respectively. It can be concluded that the proposed method can effectively improve the classification performance of narrowband radar and the discrimination ability against bait targets, thereby improving the reliability of recognition results.
Narrowband radar is widely used in the field of air defense guidance due to its advantages of low cost and long operating range. With the development of high-speed mobile platforms, traditional target recognition methods based on feature modeling of long-term observation echo sequences are no longer applicable. In response to the problem of poor feature recognition ability of narrowband radar for Observe Echoes for a Short period of Time (OEST) sequences and susceptibility to bait target interference, resulting in low reliability of recognition results, a narrowband radar OEST sequence air target recognition method using multi feature adaptive fusion is proposed in this paper. Firstly, the encoder and classification layers are constructed with channel-spatial attention modules and trained to adaptively enhance features with high separability. Then, the maximum edge orthogonal loss function is proposed to increase the feature spacing between different classes, reduce the feature spacing between the same classes, and make the feature vectors orthogonal between different classes; Finally, the parameters of the encoder layer and classification layer are fixed, and the decoder layer is trained using reconstruction loss value to ensure that the model has accurate identification ability for decoy targets. Under the condition of an observation sequence length of 100, the classification accuracy and discrimination rate of the experimental part reached 94.37% and 96.78%, respectively. It can be concluded that the proposed method can effectively improve the classification performance of narrowband radar and the discrimination ability against bait targets, thereby improving the reliability of recognition results.
Available online , doi: 10.11999/JEIT231201
Abstract:
An overview of multimodal communication as an important information transfer mode that can simultaneously interact with multiple modal forms in different application scenarios is proposed in this paper. The future development prospects of multimodal communication in 6G wireless communication technology is also discussed. Firstly, multimodal communication is classified into three categories, and its key roles in these fields are explored. Furthermore, a deep analysis is conducted on the communication, sensation, computation, and storage resource limitations, as well as cross-domain resource management issues that 6G wireless communication systems may face. It points out that future 6G wireless multimodal communication will achieve deep integration of communication perception, computation, and storage, as well as enhance communication capabilities. In the process of implementing multimodal communication, various aspects must be considered, including multi-transmitter processing, transmission technology, and receiver processing, in order to address challenges in multimodal corpus construction, multimodal information compression, transmission, interference handling, noise reduction, alignment, fusion, and expansion, as well as resource management issues. Finally, the importance of cross-domain multimodal information transfer, complementarity, and collaboration in the 6G network is emphasized. This will better integrate and apply a massive amount of heterogeneous information to meet the future communication demands of high-speed, low-latency, and intelligent interconnection.
An overview of multimodal communication as an important information transfer mode that can simultaneously interact with multiple modal forms in different application scenarios is proposed in this paper. The future development prospects of multimodal communication in 6G wireless communication technology is also discussed. Firstly, multimodal communication is classified into three categories, and its key roles in these fields are explored. Furthermore, a deep analysis is conducted on the communication, sensation, computation, and storage resource limitations, as well as cross-domain resource management issues that 6G wireless communication systems may face. It points out that future 6G wireless multimodal communication will achieve deep integration of communication perception, computation, and storage, as well as enhance communication capabilities. In the process of implementing multimodal communication, various aspects must be considered, including multi-transmitter processing, transmission technology, and receiver processing, in order to address challenges in multimodal corpus construction, multimodal information compression, transmission, interference handling, noise reduction, alignment, fusion, and expansion, as well as resource management issues. Finally, the importance of cross-domain multimodal information transfer, complementarity, and collaboration in the 6G network is emphasized. This will better integrate and apply a massive amount of heterogeneous information to meet the future communication demands of high-speed, low-latency, and intelligent interconnection.
Available online , doi: 10.11999/JEIT231137
Abstract:
With the rapid developments of positioning systems, high-precision and low-cost direction-finding technologies are urgently needed. The hardware complexity and economic cost of traditional direction-finding methods have hindered their wide applications. Recently, direction finding based on Time-Modulated Arrays (TMAs) has overcome the shortcomings of traditional direction-finding methods. Nevertheless, to ensure measurement accuracy, one has to keep an adequate number of array elements in common TMAs. Consequently, a question arises, i.e., is it possible to reduce the number of array elements in TMAs, thus making the hardware complexity as low as possible? A novel direction-finding method based on the TMA with a single radio frequency channel and composite baselines is proposed in this paper. In the method, four antennas are meticulously arranged at specific intervals to form double-long baselines, and accurate and low-cost direction finding is realized with the ingenious usage of field programmable gate array and single receiving channel. To verify the effectiveness of the method, a prototype system in the S band is designed, fabricated, and measured. Detailed comparisons with the existing methods are provided. The work will benefit the development and application of high-precision and low-cost direction-finding systems.
With the rapid developments of positioning systems, high-precision and low-cost direction-finding technologies are urgently needed. The hardware complexity and economic cost of traditional direction-finding methods have hindered their wide applications. Recently, direction finding based on Time-Modulated Arrays (TMAs) has overcome the shortcomings of traditional direction-finding methods. Nevertheless, to ensure measurement accuracy, one has to keep an adequate number of array elements in common TMAs. Consequently, a question arises, i.e., is it possible to reduce the number of array elements in TMAs, thus making the hardware complexity as low as possible? A novel direction-finding method based on the TMA with a single radio frequency channel and composite baselines is proposed in this paper. In the method, four antennas are meticulously arranged at specific intervals to form double-long baselines, and accurate and low-cost direction finding is realized with the ingenious usage of field programmable gate array and single receiving channel. To verify the effectiveness of the method, a prototype system in the S band is designed, fabricated, and measured. Detailed comparisons with the existing methods are provided. The work will benefit the development and application of high-precision and low-cost direction-finding systems.
Available online , doi: 10.11999/JEIT231333
Abstract:
In modern radar technology, a key research area is the design of special waveforms to prevent non-cooperative electronic reconnaissance systems from intercepting and detecting radar signals. This paper focuses on reducing the power interception probability of electronic reconnaissance systems while maintaining the radiation energy. Specifically, waveform design techniques are explored for passive countermeasures, considering the time-frequency distribution of energy and the characteristics of Short-Time Fourier Transform (STFT) wideband digital reconnaissance receivers. To address this, a low-intercept model for STFT wideband digital reconnaissance receiver is established, and then the low-intercept problem is converted into a constant envelope sequence iterative optimization problem using a quadratic optimization model. To improve autocorrelation performance, an auxiliary scalar is employed to transform the optimization model into a quadratic form and a sequence of low-interception waveforms are generated through an iterative algorithm. Furthermore, the computational complexity of our proposed method is discussed. The simulation results, demonstrate that our sequence exhibits superior low-interception capability compared to commonly used phase-coded signals at the same receive Signal-to-Noise Ratio (SNR). Additionally, we introduce Pareto weights are introduced to control the autocorrelation characteristics of the proposed sequence, thereby enhancing the design flexibility.
In modern radar technology, a key research area is the design of special waveforms to prevent non-cooperative electronic reconnaissance systems from intercepting and detecting radar signals. This paper focuses on reducing the power interception probability of electronic reconnaissance systems while maintaining the radiation energy. Specifically, waveform design techniques are explored for passive countermeasures, considering the time-frequency distribution of energy and the characteristics of Short-Time Fourier Transform (STFT) wideband digital reconnaissance receivers. To address this, a low-intercept model for STFT wideband digital reconnaissance receiver is established, and then the low-intercept problem is converted into a constant envelope sequence iterative optimization problem using a quadratic optimization model. To improve autocorrelation performance, an auxiliary scalar is employed to transform the optimization model into a quadratic form and a sequence of low-interception waveforms are generated through an iterative algorithm. Furthermore, the computational complexity of our proposed method is discussed. The simulation results, demonstrate that our sequence exhibits superior low-interception capability compared to commonly used phase-coded signals at the same receive Signal-to-Noise Ratio (SNR). Additionally, we introduce Pareto weights are introduced to control the autocorrelation characteristics of the proposed sequence, thereby enhancing the design flexibility.
Available online , doi: 10.11999/JEIT231338
Abstract:
Realizing high accuracy and low computational burden is a serious challenge faced by Convolutional Neural Network (CNN) for real-time semantic segmentation. In this paper, an efficient real-time semantic segmentation Adaptive Attention mechanism Fusion Network(AAFNet) is designed for complex urban street scenes with numerous types of targets and large changes in lighting. Image spatial details and semantic information are respectively extracted by the network, and then, through Feature Fusion Network(FFN), accurate semantic images are obtained. Dilated Deep-Wise separable convolution (DDW) is adopted by AAFNet to increase the receptive field of semantic feature extraction, an Adaptive Attention mechanism Fusion Module (AAFM) is proposed, which combines Adaptive average pooling(Avp) and Adaptive max pooling(Amp) to refine the edge segmentation effect of the target and reduce the leakage rate of small targets. Finally, semantic segmentation experiments are performed on the Cityscapes and CamVid datasets for complex urban street scenes. The designed AAFNet achieves 73.0% and 69.8% mean Intersection over Union (mIoU) at inference speeds of 32 fps (Cityscapes) and 52 fps (CamVid). Compared with Dilated Spatial Attention Network (DSANet), Multi-Scale Context Fusion Network (MSCFNet), and Lightweight Bilateral Asymmetric Residual Network (LBARNet), AAFNet has the highest segmentation accuracy.
Realizing high accuracy and low computational burden is a serious challenge faced by Convolutional Neural Network (CNN) for real-time semantic segmentation. In this paper, an efficient real-time semantic segmentation Adaptive Attention mechanism Fusion Network(AAFNet) is designed for complex urban street scenes with numerous types of targets and large changes in lighting. Image spatial details and semantic information are respectively extracted by the network, and then, through Feature Fusion Network(FFN), accurate semantic images are obtained. Dilated Deep-Wise separable convolution (DDW) is adopted by AAFNet to increase the receptive field of semantic feature extraction, an Adaptive Attention mechanism Fusion Module (AAFM) is proposed, which combines Adaptive average pooling(Avp) and Adaptive max pooling(Amp) to refine the edge segmentation effect of the target and reduce the leakage rate of small targets. Finally, semantic segmentation experiments are performed on the Cityscapes and CamVid datasets for complex urban street scenes. The designed AAFNet achieves 73.0% and 69.8% mean Intersection over Union (mIoU) at inference speeds of 32 fps (Cityscapes) and 52 fps (CamVid). Compared with Dilated Spatial Attention Network (DSANet), Multi-Scale Context Fusion Network (MSCFNet), and Lightweight Bilateral Asymmetric Residual Network (LBARNet), AAFNet has the highest segmentation accuracy.
Available online , doi: 10.11999/JEIT231165
Abstract:
To relieve the impact of data heterogeneity problems caused by full overlapping attribute skew between clients in Federated Learning (FL), a local adaptive FL algorithm that incorporates channel personalized normalization is proposed in this paper. Specifically, an FL model oriented to data attribute skew is constructed, and a series of random enhancement operations are performed on the images data set in the client before training begins. Next, the client calculates the mean and standard deviation of the data set separately by color channel to achieve channel personalized normalization. Furthermore, a local adaptive update FL algorithm is designed, that is, the global model and the local model are adaptively aggregated for local initialization. The uniqueness of this aggregation method is that it not only retains the personalized characteristics of the client model, but also can capture necessary information in the global model to improve the generalization performance of the model. Finally, the experimental results demonstrate that the proposed algorithm obtains competitive convergence speed compared with existing representative works and the accuracy is 3%～19% higher.
To relieve the impact of data heterogeneity problems caused by full overlapping attribute skew between clients in Federated Learning (FL), a local adaptive FL algorithm that incorporates channel personalized normalization is proposed in this paper. Specifically, an FL model oriented to data attribute skew is constructed, and a series of random enhancement operations are performed on the images data set in the client before training begins. Next, the client calculates the mean and standard deviation of the data set separately by color channel to achieve channel personalized normalization. Furthermore, a local adaptive update FL algorithm is designed, that is, the global model and the local model are adaptively aggregated for local initialization. The uniqueness of this aggregation method is that it not only retains the personalized characteristics of the client model, but also can capture necessary information in the global model to improve the generalization performance of the model. Finally, the experimental results demonstrate that the proposed algorithm obtains competitive convergence speed compared with existing representative works and the accuracy is 3%～19% higher.
Available online , doi: 10.11999/JEIT231274
Abstract:
Deep learning methods have gained popularity in multimodal sentiment analysis due to their impressive representation and fusion capabilities in recent years. Existing studies often analyze the emotions of individuals using multimodal information such as text, facial expressions, and speech intonation, primarily employing complex fusion methods. However, existing models inadequately consider the dynamic changes in emotions over long time sequences, resulting in suboptimal performance in sentiment analysis. In response to this issue, a Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning is proposed in this paper. Firstly, the paper employs long-term textual information to enable the model to learn dynamic changes in audio and video across extended time sequences. Subsequently, a gating mechanism is employed to eliminate redundant information and semantic ambiguity between modalities. Finally, contrastive learning is applied to strengthen the interaction between modalities, enhancing the model’s generalization. Experimental results demonstrate that on the CMU-MOSI dataset, the model improves the Pearson Correlation coefficient (Corr) and F1 score by 3.7% and 2.1%, respectively. On the CMU-MOSEI dataset, the model increases “Corr” and “F1 score” by 1.4% and 1.1%, respectively. Therefore, the proposed model effectively utilizes intermodal interaction information while eliminating information redundancy.
Deep learning methods have gained popularity in multimodal sentiment analysis due to their impressive representation and fusion capabilities in recent years. Existing studies often analyze the emotions of individuals using multimodal information such as text, facial expressions, and speech intonation, primarily employing complex fusion methods. However, existing models inadequately consider the dynamic changes in emotions over long time sequences, resulting in suboptimal performance in sentiment analysis. In response to this issue, a Multimodal Sentiment Analysis Model Enhanced with Non-verbal Information and Contrastive Learning is proposed in this paper. Firstly, the paper employs long-term textual information to enable the model to learn dynamic changes in audio and video across extended time sequences. Subsequently, a gating mechanism is employed to eliminate redundant information and semantic ambiguity between modalities. Finally, contrastive learning is applied to strengthen the interaction between modalities, enhancing the model’s generalization. Experimental results demonstrate that on the CMU-MOSI dataset, the model improves the Pearson Correlation coefficient (Corr) and F1 score by 3.7% and 2.1%, respectively. On the CMU-MOSEI dataset, the model increases “Corr” and “F1 score” by 1.4% and 1.1%, respectively. Therefore, the proposed model effectively utilizes intermodal interaction information while eliminating information redundancy.
Available online , doi: 10.11999/JEIT231170
Abstract:
To comprehensively explore the information content of camouflaged target features, leverage the potential of target detection algorithms, and address issues such as low camouflage target detection accuracy and high false positive rates, a camouflage target detection algorithm named CAFM-YOLOv5 (Cross Attention Fusion Module Based on YOLOv5) is proposed. Firstly, a camouflaged target multispectral dataset is constructed for the performance validation of the multimodal image fusion method; secondly, a dual-stream convolution channel is constructed for visible and infrared image feature extraction; and finally, a cross-attention fusion module is proposed based on the channel-attention mechanism and spatial-attention mechanism in order to realise the effective fusion of two different features.Experimental results demonstrate that the model achieves a detection accuracy of 96.4% and a recognition probability of 88.1%, surpassing the YOLOv5 baseline network. Moreover, when compared with unimodal detection algorithms like YOLOv8 and multimodal detection algorithms such as SLBAF-Net, our algorithm exhibits superior performance in detection accuracy metrics. These findings highlight the practical value of our method for military target detection on the battlefield, enhancing situational awareness capabilities significantly.
To comprehensively explore the information content of camouflaged target features, leverage the potential of target detection algorithms, and address issues such as low camouflage target detection accuracy and high false positive rates, a camouflage target detection algorithm named CAFM-YOLOv5 (Cross Attention Fusion Module Based on YOLOv5) is proposed. Firstly, a camouflaged target multispectral dataset is constructed for the performance validation of the multimodal image fusion method; secondly, a dual-stream convolution channel is constructed for visible and infrared image feature extraction; and finally, a cross-attention fusion module is proposed based on the channel-attention mechanism and spatial-attention mechanism in order to realise the effective fusion of two different features.Experimental results demonstrate that the model achieves a detection accuracy of 96.4% and a recognition probability of 88.1%, surpassing the YOLOv5 baseline network. Moreover, when compared with unimodal detection algorithms like YOLOv8 and multimodal detection algorithms such as SLBAF-Net, our algorithm exhibits superior performance in detection accuracy metrics. These findings highlight the practical value of our method for military target detection on the battlefield, enhancing situational awareness capabilities significantly.
Available online , doi: 10.11999/JEIT231048
Abstract:
Dissimilar redundancy architecture is widely used in information systems to improve their security and reliability. When the system operates normally, the executers behave consistently, but when faced with malicious attacks, the executers exhibit inconsistency. The architecture improves the security and reliability of the system by comparing the performance of the executers to monitor the system and perceive threats. The synchronization of executers is a challenge that all dissimilar redundancy architectures need to address. There is currently no systematic description and summary of synchronization technology. This article is a review of executer synchronization techniques in dissimilar redundancy architectures. First, the importance of synchronization in dissimilar redundancy systems is explained and a standardized description of synchronization is provided. Then, a synchronization technology classification method based on synchronization points is proposed and the basic process, popularity, advantages and disadvantages of each class are summarized separately. This article also proposes three important indicators that affect synchronization performance, namely synchronization point, false alarm rate, and performance, and provides a mathematical model for synchronization technology, which can be used for design evaluation. Finally, this article combines the development of cyber resilience and software defined system on wafer, pointing out the potential and possible directions for the future development of synchronous technology.
Dissimilar redundancy architecture is widely used in information systems to improve their security and reliability. When the system operates normally, the executers behave consistently, but when faced with malicious attacks, the executers exhibit inconsistency. The architecture improves the security and reliability of the system by comparing the performance of the executers to monitor the system and perceive threats. The synchronization of executers is a challenge that all dissimilar redundancy architectures need to address. There is currently no systematic description and summary of synchronization technology. This article is a review of executer synchronization techniques in dissimilar redundancy architectures. First, the importance of synchronization in dissimilar redundancy systems is explained and a standardized description of synchronization is provided. Then, a synchronization technology classification method based on synchronization points is proposed and the basic process, popularity, advantages and disadvantages of each class are summarized separately. This article also proposes three important indicators that affect synchronization performance, namely synchronization point, false alarm rate, and performance, and provides a mathematical model for synchronization technology, which can be used for design evaluation. Finally, this article combines the development of cyber resilience and software defined system on wafer, pointing out the potential and possible directions for the future development of synchronous technology.
Available online , doi: 10.11999/JEIT231033
Abstract:
The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users' data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, we first derive the closed-form expression of each user’s optimal transmit power. Then we use the Successive Convex Approximation (SCA) method to approximate the non-convex constraints of the formulated problem, and propose a SCA based efficient iterative algorithm to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
The system energy consumption minimization problem is studied for a data compression based Non-Orthogonal Multiple Access-Mobile Edge Computing (NOMA-MEC) system. Considering the partial compression and offloading schemes and the limited computation capacity at the base station, a system energy consumption minimization optimization problem is formulated by jointly optimizing the users' data compression and offloading ratios, transmit power, data compression time, etc. In order to solve this problem, we first derive the closed-form expression of each user’s optimal transmit power. Then we use the Successive Convex Approximation (SCA) method to approximate the non-convex constraints of the formulated problem, and propose a SCA based efficient iterative algorithm to solve the formulated problem, obtaining the optimal resource allocation scheme of the system. Finally, the simulation results verify the advantages of the proposed scheme via computer simulations and show that compared with other benchmark schemes, the proposed scheme can effectively reduce the system energy consumption.
Available online , doi: 10.11999/JEIT231262
Abstract:
An end-to-end quadruple Super-Resolution Inpainting Generative Adversarial Network (SRIGAN) is proposed in this paper, for low-resolution random occlusion face images. The generative network consists of an encoder, a feature compensation subnetwork, and a decoder constructed with a pyramid attention module. The discriminant network is an improved Patch discriminant network. The network can effectively learn the absent features of the occluded region through a feature compensation subnetwork and a two-stage training strategy. Then, the information is constructed with the decoder with a pyramid attention module and multi-scale reconstruction loss. Hence, the generative network can transform a low-resolution occlusion image into a quadruple high-resolution complete image. Furthermore, the improvements of the loss function and Patch discriminant network are employed to ensure the stability of network training and enhance the performance of the generated network. The effectiveness of the proposed algorithm is verified by comparison and module verification experiments.
An end-to-end quadruple Super-Resolution Inpainting Generative Adversarial Network (SRIGAN) is proposed in this paper, for low-resolution random occlusion face images. The generative network consists of an encoder, a feature compensation subnetwork, and a decoder constructed with a pyramid attention module. The discriminant network is an improved Patch discriminant network. The network can effectively learn the absent features of the occluded region through a feature compensation subnetwork and a two-stage training strategy. Then, the information is constructed with the decoder with a pyramid attention module and multi-scale reconstruction loss. Hence, the generative network can transform a low-resolution occlusion image into a quadruple high-resolution complete image. Furthermore, the improvements of the loss function and Patch discriminant network are employed to ensure the stability of network training and enhance the performance of the generated network. The effectiveness of the proposed algorithm is verified by comparison and module verification experiments.
Available online , doi: 10.11999/JEIT231175
Abstract:
In many monitoring and control tasks, it is difficult for the control center to get the real-time status information directly because of the distance between the monitored target and the control center. The Unmanned Aerial Vehicles (UAV) can make full use of its advantages of high mobility, reduce the sensing and communication distance, and then improve the sensing and communication capabilities, which provides a new idea for real-time acquisition of remote target status information. In this paper, the optimization problem of Age of Information (AoI) analysis in UAV-assisted integrated sensing and communication system is studied. Firstly, the status update process of control center is analyzed, and then the closed-form expression of average peak AoI is derived. Further, in the multi-UAV multi-target scenario, the average peak AoI of the system is further reduced by optimizing the perception position and communication position of the UAV in the air, as well as the matching relationship between the UAV and the target, and the real-time status update is improved. The simulation results verify the correctness of the theoretical analysis, and show that the proposed optimization method can effectively improve the AoI performance of the system compared with the benchmark methods.
In many monitoring and control tasks, it is difficult for the control center to get the real-time status information directly because of the distance between the monitored target and the control center. The Unmanned Aerial Vehicles (UAV) can make full use of its advantages of high mobility, reduce the sensing and communication distance, and then improve the sensing and communication capabilities, which provides a new idea for real-time acquisition of remote target status information. In this paper, the optimization problem of Age of Information (AoI) analysis in UAV-assisted integrated sensing and communication system is studied. Firstly, the status update process of control center is analyzed, and then the closed-form expression of average peak AoI is derived. Further, in the multi-UAV multi-target scenario, the average peak AoI of the system is further reduced by optimizing the perception position and communication position of the UAV in the air, as well as the matching relationship between the UAV and the target, and the real-time status update is improved. The simulation results verify the correctness of the theoretical analysis, and show that the proposed optimization method can effectively improve the AoI performance of the system compared with the benchmark methods.
Available online , doi: 10.11999/JEIT231004
Abstract:
To achieve formation control of multiple snake robots, an error-constrained anti-interference path-following method is proposed in this paper. A highly coupled dynamic frequency compensator is used to adjust the motion speed of each robot to ensure consistency in the position and velocity of the formation members. In dynamic control, the singularity phenomenon of virtual variables is eliminated by the equivalent principle of barrier functions, improving the stability of path following. In addition, predictive values for model uncertainty and external interference are designed to pre-compensate for joint offsets and torque inputs of the robots, further improving the convergence rate and steady-state performance of the following errors. Finally, the Lyapunov theory is used to prove the Uniform Ultimate Boundedness (UUB) of this system. Simulation data demonstrate that the proposed method and control strategy have higher following accuracy compared to other classic methods.
To achieve formation control of multiple snake robots, an error-constrained anti-interference path-following method is proposed in this paper. A highly coupled dynamic frequency compensator is used to adjust the motion speed of each robot to ensure consistency in the position and velocity of the formation members. In dynamic control, the singularity phenomenon of virtual variables is eliminated by the equivalent principle of barrier functions, improving the stability of path following. In addition, predictive values for model uncertainty and external interference are designed to pre-compensate for joint offsets and torque inputs of the robots, further improving the convergence rate and steady-state performance of the following errors. Finally, the Lyapunov theory is used to prove the Uniform Ultimate Boundedness (UUB) of this system. Simulation data demonstrate that the proposed method and control strategy have higher following accuracy compared to other classic methods.
Available online , doi: 10.11999/JEIT231111
Abstract:
Short Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes without small cycles suitable for an arbitrary row weight (i.e., Row-Weight Universal (RWU)), are of great significance for both theoretical research and engineering application. Existing methods having RWU property and guaranteeing the nonexistence of 4-cycles and 6-cycles, can only offer short QC-LDPC codes for the column weights of 3 and 4. Based on the Greatest Common Divisor (GCD) framework, three new methods are proposed in this paper for the column weights of 5 and 6, which can possess RWU property and at the same time remove all 4-cycles and 6-cycles. Compared with existing methods with RWU property, the code lengths of the novel methods are sharply reduced from the fourth power of row weight to the third power of row weight. Therefore, the new methods can provide short RWU QC-LDPC codes without 4-cycles and 6-cycles for occasions where base codes with large column weights are required, such as composite constructions and advanced optimization pertaining to QC-LDPC codes. Moreover, compared with the search-based symmetric QC-LDPC codes, the new codes need no search, have lower description complexity, and exhibit better decoding performance.
Short Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes without small cycles suitable for an arbitrary row weight (i.e., Row-Weight Universal (RWU)), are of great significance for both theoretical research and engineering application. Existing methods having RWU property and guaranteeing the nonexistence of 4-cycles and 6-cycles, can only offer short QC-LDPC codes for the column weights of 3 and 4. Based on the Greatest Common Divisor (GCD) framework, three new methods are proposed in this paper for the column weights of 5 and 6, which can possess RWU property and at the same time remove all 4-cycles and 6-cycles. Compared with existing methods with RWU property, the code lengths of the novel methods are sharply reduced from the fourth power of row weight to the third power of row weight. Therefore, the new methods can provide short RWU QC-LDPC codes without 4-cycles and 6-cycles for occasions where base codes with large column weights are required, such as composite constructions and advanced optimization pertaining to QC-LDPC codes. Moreover, compared with the search-based symmetric QC-LDPC codes, the new codes need no search, have lower description complexity, and exhibit better decoding performance.
Available online , doi: 10.11999/JEIT231103
Abstract:
In order to balance the transmission reliability and efficiency of large-scale multi-mode mesh networks in the new power system, a two-stage algorithm is proposed based on reinforcement learning for joint routing selection and resource scheduling in large-scale multi-mode mesh networks, building upon the description and analysis of optimization problems. In the first stage, based on the network topology information and service requirements, a multi shortest path routing algorithm is utilized to generate all the shortest paths. In the second stage, a resource scheduling algorithm based on Multi-Armed Bandit (MAB) is proposed. The algorithm constructs the arms of the MAB based on the obtained set of shortest paths, then calculates the reward according to the service demands, and finally gives the optimal route selection and resource scheduling mode for service transmission. Simulation results show that the proposed algorithm can meet different service transmission requirements, and achieve an efficient balance between the average end-to-end path delay and the average transmission success rate.
In order to balance the transmission reliability and efficiency of large-scale multi-mode mesh networks in the new power system, a two-stage algorithm is proposed based on reinforcement learning for joint routing selection and resource scheduling in large-scale multi-mode mesh networks, building upon the description and analysis of optimization problems. In the first stage, based on the network topology information and service requirements, a multi shortest path routing algorithm is utilized to generate all the shortest paths. In the second stage, a resource scheduling algorithm based on Multi-Armed Bandit (MAB) is proposed. The algorithm constructs the arms of the MAB based on the obtained set of shortest paths, then calculates the reward according to the service demands, and finally gives the optimal route selection and resource scheduling mode for service transmission. Simulation results show that the proposed algorithm can meet different service transmission requirements, and achieve an efficient balance between the average end-to-end path delay and the average transmission success rate.
Available online , doi: 10.11999/JEIT231093
Abstract:
As an ideal designated verifier signature, Chameleon Signature (CS) can solve the problem of signature secondary transmission more subtly by embedding an efficient Chameleon Hash Function (CHF) into the signing algorithm. In addition to non-transferability, CS also should satisfy unforgeability, deniability, non-repudiation for the signer, and so on. To solve the problems that cryptosystems based on the traditional number theory problems, such as the large integer factorization or discrete logarithm cannot resist quantum computing attacks, and the schemes that provably secure in the random oracle model may not be secure in a practical implementation, a lattice-based CS scheme in the standard model is proposed; Furthermore, to solve the problem of requiring a significant local storage to obtain deniability for the signer, a lattice-based CS scheme without local storage in the standard model is proposed, the new scheme completely eliminates the signer's dependence on the local signature library, and enables the signer to assist an arbitrator to reject a forged signature of any adversary without storing the original message and signature. Particularly, based on the hardness of the small integer solution problem and learning with errors problem, both schemes are proved secure in the standard model.
As an ideal designated verifier signature, Chameleon Signature (CS) can solve the problem of signature secondary transmission more subtly by embedding an efficient Chameleon Hash Function (CHF) into the signing algorithm. In addition to non-transferability, CS also should satisfy unforgeability, deniability, non-repudiation for the signer, and so on. To solve the problems that cryptosystems based on the traditional number theory problems, such as the large integer factorization or discrete logarithm cannot resist quantum computing attacks, and the schemes that provably secure in the random oracle model may not be secure in a practical implementation, a lattice-based CS scheme in the standard model is proposed; Furthermore, to solve the problem of requiring a significant local storage to obtain deniability for the signer, a lattice-based CS scheme without local storage in the standard model is proposed, the new scheme completely eliminates the signer's dependence on the local signature library, and enables the signer to assist an arbitrator to reject a forged signature of any adversary without storing the original message and signature. Particularly, based on the hardness of the small integer solution problem and learning with errors problem, both schemes are proved secure in the standard model.
Available online , doi: 10.11999/JEIT231145
Abstract:
Elements such as pulse interference and outlier measurement information usually lead to abnormal heavy-tailed noise, which sharply reduces the performance of the Extended Target Tracking (ETT) estimator based on the Gaussian hypothesis. To address this problem, a Student’s t Inverse Wishart Smoothing (StIWS) algorithm based on the Random Matrix Model (RMM) is proposed. Firstly, the kinematic state of the target, process noise and measurement noise are modeled as a Student’s t distribution to characterize the effect of anomalous noise on the probability distribution of extended target, and the extended state of target is modeled as a random matrix which obeys inverse Wishart distribution. Then, in a Student’s t bayesian smoothing frame, the StIWS algorithm is derived in detail, which can effectively estimate target state in the process of the dynamic evolution of multiple characteristics of extended target. Finally, the effectiveness of the proposed algorithm is verified by the simulation experiment and the engineering experiment of extended target tracking.
Elements such as pulse interference and outlier measurement information usually lead to abnormal heavy-tailed noise, which sharply reduces the performance of the Extended Target Tracking (ETT) estimator based on the Gaussian hypothesis. To address this problem, a Student’s t Inverse Wishart Smoothing (StIWS) algorithm based on the Random Matrix Model (RMM) is proposed. Firstly, the kinematic state of the target, process noise and measurement noise are modeled as a Student’s t distribution to characterize the effect of anomalous noise on the probability distribution of extended target, and the extended state of target is modeled as a random matrix which obeys inverse Wishart distribution. Then, in a Student’s t bayesian smoothing frame, the StIWS algorithm is derived in detail, which can effectively estimate target state in the process of the dynamic evolution of multiple characteristics of extended target. Finally, the effectiveness of the proposed algorithm is verified by the simulation experiment and the engineering experiment of extended target tracking.
Available online , doi: 10.11999/JEIT231160
Abstract:
Traditional multireceiver Synthetic Aperture Sonar (SAS) imaging algorithms based on Phase Center Approximation (PCA) neglect the spatial variance of approximation error in the azimuth dimension. The distortion would be introduced in the focused results of distributed. To solve this problem, a two-way slant range considering the azimuth variance of approximation error is deduced based on the geometry models of transmitter/receiver bistatic sampling and PCA sampling. The system function in the 2D frequency domain is further decomposed into transmitter/receiver bistatic deformation term and quasi monostatic term. Based on that, the complex multiplication and interpolation are adopted to compensate the bistatic deformation term. Then, the range-Doppler imaging algorithm is used to focus the targets. Compared to traditional methods, much smaller appropriation error across the whole mapping swath is obtained by using the proposed. Besides, the position deviation in the azimuth dimension is not introduced by the proposed method. The imaging result which is identical to practical target position can be obtained.
Traditional multireceiver Synthetic Aperture Sonar (SAS) imaging algorithms based on Phase Center Approximation (PCA) neglect the spatial variance of approximation error in the azimuth dimension. The distortion would be introduced in the focused results of distributed. To solve this problem, a two-way slant range considering the azimuth variance of approximation error is deduced based on the geometry models of transmitter/receiver bistatic sampling and PCA sampling. The system function in the 2D frequency domain is further decomposed into transmitter/receiver bistatic deformation term and quasi monostatic term. Based on that, the complex multiplication and interpolation are adopted to compensate the bistatic deformation term. Then, the range-Doppler imaging algorithm is used to focus the targets. Compared to traditional methods, much smaller appropriation error across the whole mapping swath is obtained by using the proposed. Besides, the position deviation in the azimuth dimension is not introduced by the proposed method. The imaging result which is identical to practical target position can be obtained.
Available online , doi: 10.11999/JEIT231061
Abstract:
To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence.
Available online , doi: 10.11999/JEIT230942
Abstract:
Considering the secure communication of the distributed Reconfigurable Intelligent Surfaces (RISs) under imperfect Channel State Information (CSI), a joint optimization problem of the secrecy rate maximization based on the active beamforming, Artificial Noise(AN), and RISs’ phase shifts is formulated. Then an efficient algorithm based on alternating optimization and 1-Dimensional linear search is proposed to solve the non-convex optimization problem. Simulation results demonstrate that, compared with the random phase optimization scheme and the secure transmission without AN scheme, the proposed scheme can achieve a higher secrecy rate. The superiority of the proposed scheme over the other transmission schemes becomes more prominent with the increase of the number of distribution units. The proposed scheme has better robustness than the other transmission schemes to the uncertainty of communication channel in our considered network.
Considering the secure communication of the distributed Reconfigurable Intelligent Surfaces (RISs) under imperfect Channel State Information (CSI), a joint optimization problem of the secrecy rate maximization based on the active beamforming, Artificial Noise(AN), and RISs’ phase shifts is formulated. Then an efficient algorithm based on alternating optimization and 1-Dimensional linear search is proposed to solve the non-convex optimization problem. Simulation results demonstrate that, compared with the random phase optimization scheme and the secure transmission without AN scheme, the proposed scheme can achieve a higher secrecy rate. The superiority of the proposed scheme over the other transmission schemes becomes more prominent with the increase of the number of distribution units. The proposed scheme has better robustness than the other transmission schemes to the uncertainty of communication channel in our considered network.
Available online , doi: 10.11999/JEIT231082
Abstract:
Considering the problem of the non-uniformity of the ramp signal in the large-array CMOS Image Sensors (CIS), a ramp uniformity correction method for CMOS image sensors is proposed in this paper. The correction method is based on error storage and level shift ideas. Storage capacitor that are used to store ramp non-uniformity error are introduced in column readout circuit. According to the stored ramp non-uniformity error, the ramp signal of each column is shifted. So as to ensure the uniformity of the ramp signal. Based on the 55 nm 1P4M CMOS process, this paper has completed the detailed circuit design and comprehensive simulation verification of the proposed method. Under the design conditions that the voltage range of the ramp signal is 1.4 V, the slope of the ramp signal is 71.908 V/ms, the number of pixel area arrays is 8192(H)×8192(V), and a single pixel size is 10 μm, the proposed correction method reduces the ramp non-uniformity error from 7.89mV to 36.8 μV. The Differential NonLinearity (DNL) of the ramp signal is +0.001 3/–0.004 LSB and the Integral NonLinearity (INL) is +0.045/–0.02 LSB. The Column Fixed Pattern Noise(CFPN) is reduced from 1.9% to 0.01%. The proposed ramp uniformity correction method reduces the ramp non-uniformity error by 99.54% on the basis of ensuring the high linearity of the ramp signal, without significantly increasing the chip area and without introducing additional power consumption. It provides a certain theoretical support for the design of high-precision CMOS image sensors.
Considering the problem of the non-uniformity of the ramp signal in the large-array CMOS Image Sensors (CIS), a ramp uniformity correction method for CMOS image sensors is proposed in this paper. The correction method is based on error storage and level shift ideas. Storage capacitor that are used to store ramp non-uniformity error are introduced in column readout circuit. According to the stored ramp non-uniformity error, the ramp signal of each column is shifted. So as to ensure the uniformity of the ramp signal. Based on the 55 nm 1P4M CMOS process, this paper has completed the detailed circuit design and comprehensive simulation verification of the proposed method. Under the design conditions that the voltage range of the ramp signal is 1.4 V, the slope of the ramp signal is 71.908 V/ms, the number of pixel area arrays is 8192(H)×8192(V), and a single pixel size is 10 μm, the proposed correction method reduces the ramp non-uniformity error from 7.89mV to 36.8 μV. The Differential NonLinearity (DNL) of the ramp signal is +0.001 3/–0.004 LSB and the Integral NonLinearity (INL) is +0.045/–0.02 LSB. The Column Fixed Pattern Noise(CFPN) is reduced from 1.9% to 0.01%. The proposed ramp uniformity correction method reduces the ramp non-uniformity error by 99.54% on the basis of ensuring the high linearity of the ramp signal, without significantly increasing the chip area and without introducing additional power consumption. It provides a certain theoretical support for the design of high-precision CMOS image sensors.
Available online , doi: 10.11999/JEIT230716
Abstract:
An air-ground collaborative integrated sensing and communication system is studied, where the air-ground collaborative network is composed of an Unmanned Ground Vehicle (UGV) base station and Unmanned Aerial Vehicle (UAV) relays. The network provides communication service for ground users while detecting and sensing target areas. The air-ground channels are modeled as the accurate Rician fading channel model. On the constraints of the sensing frequency and the effective sensing power threshold of the target areas, the minimum average communication rate of all users is maximized by jointly optimizing the communication and sensing association of the system, the transmit power and flight trajectory of the UAV relays, as well as the transmit power and trajectory of the UGV base station. To solve the resultant non-convex integer optimization problem with highly coupled variables, the block coordinate descent method is applied to decompose the original optimization problem into four sub-problems, where relaxation variables are introduced, and the integer constraints are converted into penalty terms. Then, it is proved that the effective sensing power is a composition function of the trajectory variables and the relaxation variables and is a jointly convex function of them, so that the non-convex terms are tackled by using the successive convex optimization method. Lastly, a two-layer iterative algorithm is proposed to obtain the suboptimal solution efficiently. It is showed by simulation results that as compared to some benchmark algorithms, the proposed algorithm significantly increases the minimum average communication rate of all users while achieving the same sensing performance and achieves a better performance trade-off between communication and sensing with good convergence performance.
An air-ground collaborative integrated sensing and communication system is studied, where the air-ground collaborative network is composed of an Unmanned Ground Vehicle (UGV) base station and Unmanned Aerial Vehicle (UAV) relays. The network provides communication service for ground users while detecting and sensing target areas. The air-ground channels are modeled as the accurate Rician fading channel model. On the constraints of the sensing frequency and the effective sensing power threshold of the target areas, the minimum average communication rate of all users is maximized by jointly optimizing the communication and sensing association of the system, the transmit power and flight trajectory of the UAV relays, as well as the transmit power and trajectory of the UGV base station. To solve the resultant non-convex integer optimization problem with highly coupled variables, the block coordinate descent method is applied to decompose the original optimization problem into four sub-problems, where relaxation variables are introduced, and the integer constraints are converted into penalty terms. Then, it is proved that the effective sensing power is a composition function of the trajectory variables and the relaxation variables and is a jointly convex function of them, so that the non-convex terms are tackled by using the successive convex optimization method. Lastly, a two-layer iterative algorithm is proposed to obtain the suboptimal solution efficiently. It is showed by simulation results that as compared to some benchmark algorithms, the proposed algorithm significantly increases the minimum average communication rate of all users while achieving the same sensing performance and achieves a better performance trade-off between communication and sensing with good convergence performance.
Available online , doi: 10.11999/JEIT230830
Abstract:
Unmanned Aerial Vehicle (UAV) equipped with Intelligent Reflecting Surface (IRS) can effectively solve the problem of inefficient information and energy transmission between the hybrid access point and nodes in complex wireless scenarios due to obstacle occlusion. A novel framework for aerial IRS-assisted wireless powered communication networks is proposed that exploits the flexibility of aerial IRS to improve the performance of the network. The architecture achieves the transmission of energy and data for each time slot employing the harvest-then-transmit scheme. A multi-variable coupled optimization problem that combines the flight trajectory, node selection association variable, time slot allocation ratio, and the phase is established while satisfying the node energy harvesting threshold. Thus, the block coordinate descent algorithm is utilized to decompose the optimization problem into four sub-problems to be solved separately. Firstly, the closed-form solution for the optimal phase of the intelligent reflecting surface is derived based on the beam alignment theory. Secondly, the non-convex problem is transformed into a convex problem by introducing auxiliary variables and employing a successive convex approximation algorithm. Finally, the solution is iteratively solved utilizing the block coordinate descent algorithm. Simulation results show that the proposed scheme has excellent convergence performance and significantly improve the average throughput.
Unmanned Aerial Vehicle (UAV) equipped with Intelligent Reflecting Surface (IRS) can effectively solve the problem of inefficient information and energy transmission between the hybrid access point and nodes in complex wireless scenarios due to obstacle occlusion. A novel framework for aerial IRS-assisted wireless powered communication networks is proposed that exploits the flexibility of aerial IRS to improve the performance of the network. The architecture achieves the transmission of energy and data for each time slot employing the harvest-then-transmit scheme. A multi-variable coupled optimization problem that combines the flight trajectory, node selection association variable, time slot allocation ratio, and the phase is established while satisfying the node energy harvesting threshold. Thus, the block coordinate descent algorithm is utilized to decompose the optimization problem into four sub-problems to be solved separately. Firstly, the closed-form solution for the optimal phase of the intelligent reflecting surface is derived based on the beam alignment theory. Secondly, the non-convex problem is transformed into a convex problem by introducing auxiliary variables and employing a successive convex approximation algorithm. Finally, the solution is iteratively solved utilizing the block coordinate descent algorithm. Simulation results show that the proposed scheme has excellent convergence performance and significantly improve the average throughput.
Available online , doi: 10.11999/JEIT230974
Abstract:
Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
Available online , doi: 10.11999/JEIT231129
Abstract:
Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security, but it is susceptible to modeling attacks based on machine learning. By studying the strong PUF circuit structure and chaotic mapping mechanism, a PUF circuit that can effectively resist machine learning modeling attacks is proposed. This circuit takes the original excitation as the initial value of the chaotic mapping, utilizes the internal relationship between the PUF excitation response mapping time and the iteration depth of the chaotic algorithm to generate unpredictable chaotic values, and uses PUF intermediate response feedback to encrypt the excitation. It can further improve the complexity of excitation and response mapping, thereby enhancing the resistance of PUF to machine learning attacks. The PUF is implemented using Artix-7 FPGA. The test results show that even with up to 1 million sets of excitation response pairs selected, the attack prediction rate based on logistic regression, support vector machine, and artificial neural network is still close to the ideal value of 50%. And the PUF has good randomness, uniqueness, and stability.
Physical Unclonable Function (PUF) has broad application prospects in the field of hardware security, but it is susceptible to modeling attacks based on machine learning. By studying the strong PUF circuit structure and chaotic mapping mechanism, a PUF circuit that can effectively resist machine learning modeling attacks is proposed. This circuit takes the original excitation as the initial value of the chaotic mapping, utilizes the internal relationship between the PUF excitation response mapping time and the iteration depth of the chaotic algorithm to generate unpredictable chaotic values, and uses PUF intermediate response feedback to encrypt the excitation. It can further improve the complexity of excitation and response mapping, thereby enhancing the resistance of PUF to machine learning attacks. The PUF is implemented using Artix-7 FPGA. The test results show that even with up to 1 million sets of excitation response pairs selected, the attack prediction rate based on logistic regression, support vector machine, and artificial neural network is still close to the ideal value of 50%. And the PUF has good randomness, uniqueness, and stability.
Available online , doi: 10.11999/JEIT231265
Abstract:
With the evolution of mobile communication technology, the Sixth-Generation (6G) wireless networks will achieve a leap from the internet of things to the internet of intelligent things, meeting higher data demands and broader application scenarios. Novel multiple access technologies and multidimensional expansion techniques will jointly play a role in 6G, providing crucial support for building an efficient, intelligent, and reliable communication network to meet the diverse demands of future communications. Therefore, this review paper aims to explore the application potentials of novel multiple access technologies in multidimensional expansion 6G communication networks. Firstly, it compares traditional multiple access technologies with potential novel multiple access technologies in 6G, with a focus on the advantages of non-orthogonal multiple access technology in improving spectral efficiency and system capacity. Then, it provides a detailed introduction to the advantages and functions of multidimensional expansion technologies such as satellite communication, Unmanned Aerial Vehicle (UAV), and Intelligent Reflecting Surface (IRS) in 6G scenarios. Furthermore, the advantages and collaborative applications of novel multiple access technologies in conjunction with satellite communication, UAV, and IRS are discussed. Finally, the paper discusses key technological challenges in a novel multi-dimensional extension network based on new multiple access technologies, including large-scale multiple-input-multiple-output, terahertz technology, integrated sensing, communication, and computing, user information security, and imperfect Channel State Information (CSI) estimation, while also providing prospects for new coding technologies, artificial intelligence and machine learning.
With the evolution of mobile communication technology, the Sixth-Generation (6G) wireless networks will achieve a leap from the internet of things to the internet of intelligent things, meeting higher data demands and broader application scenarios. Novel multiple access technologies and multidimensional expansion techniques will jointly play a role in 6G, providing crucial support for building an efficient, intelligent, and reliable communication network to meet the diverse demands of future communications. Therefore, this review paper aims to explore the application potentials of novel multiple access technologies in multidimensional expansion 6G communication networks. Firstly, it compares traditional multiple access technologies with potential novel multiple access technologies in 6G, with a focus on the advantages of non-orthogonal multiple access technology in improving spectral efficiency and system capacity. Then, it provides a detailed introduction to the advantages and functions of multidimensional expansion technologies such as satellite communication, Unmanned Aerial Vehicle (UAV), and Intelligent Reflecting Surface (IRS) in 6G scenarios. Furthermore, the advantages and collaborative applications of novel multiple access technologies in conjunction with satellite communication, UAV, and IRS are discussed. Finally, the paper discusses key technological challenges in a novel multi-dimensional extension network based on new multiple access technologies, including large-scale multiple-input-multiple-output, terahertz technology, integrated sensing, communication, and computing, user information security, and imperfect Channel State Information (CSI) estimation, while also providing prospects for new coding technologies, artificial intelligence and machine learning.
Available online , doi: 10.11999/JEIT231231
Abstract:
Integral attack is one of the most powerful cryptanalytic methods after differential and linear cryptanalysis, which was presented by Daemen et al. in 1997 (doi: 10.1007/BFb0052343). As the winning block cipher of China’s National Cipher Designing Competition in 2018, the security strength of uBlock against integral attack has received much attention. To better understand the integral property, this paper constructs the Mixed Integer Linear Programming (MILP) models for monomial prediction to search for the integral distinguishers and uses the partial sum techniques to perform key-recovery attacks. For uBlock-128/128 and uBlock-128/256, this paper gives the first 11 and 12-round attacks based on a 9-round integral distinguisher, respectively. The data complexity is\begin{document}$ {2}^{127} $\end{document} \begin{document}$ {2}^{127.06} $\end{document} \begin{document}$ {2}^{224} $\end{document} \begin{document}$ {2}^{44.58} $\end{document} \begin{document}$ {2}^{138} $\end{document} \begin{document}$ {2}^{253} $\end{document} \begin{document}$ {2}^{253.06} $\end{document} \begin{document}$ {2}^{44.46} $\end{document}
Integral attack is one of the most powerful cryptanalytic methods after differential and linear cryptanalysis, which was presented by Daemen et al. in 1997 (doi: 10.1007/BFb0052343). As the winning block cipher of China’s National Cipher Designing Competition in 2018, the security strength of uBlock against integral attack has received much attention. To better understand the integral property, this paper constructs the Mixed Integer Linear Programming (MILP) models for monomial prediction to search for the integral distinguishers and uses the partial sum techniques to perform key-recovery attacks. For uBlock-128/128 and uBlock-128/256, this paper gives the first 11 and 12-round attacks based on a 9-round integral distinguisher, respectively. The data complexity is
Available online , doi: 10.11999/JEIT230686
Abstract:
To reduce the influence of background and occlusion on the accuracy of pedestrian identity Re-IDentification (ReID) and make full use of the complementarity between fine-grained and coarse-grained information, a multi-scale occluded pedestrian ReID network guided by key fine-grained information is proposed. First, the image is divided into two types of overlapping patches with different sizes to better simulate the multi-scale characteristics of human observing images and the continuity characteristics of human observing adjacent regions, so a multi-scale recognition network containing both fine-grained and coarse-grained information extraction branches is constructed. Then, considering fine-grained information contains more details and there are similarities and differences between fine-grained and coarse-grained information, fine-grained attention module is further employed to realize the guide of the fine-grained branch to the coarse-grained branch. Among them, the fine-grained information is the key information retained after filtering out the interference information by the Interference Information Elimination (IIE) module. Finally, the key information related to pedestrian ReID is obtained by bivariate difference, and the prediction of pedestrian identity is realized by multi-dimensional joint supervision such as tags and features. Extensive experiments on several public pedestrian ReID databases prove the superiority of this algorithm and the effectiveness and necessity of each module.
To reduce the influence of background and occlusion on the accuracy of pedestrian identity Re-IDentification (ReID) and make full use of the complementarity between fine-grained and coarse-grained information, a multi-scale occluded pedestrian ReID network guided by key fine-grained information is proposed. First, the image is divided into two types of overlapping patches with different sizes to better simulate the multi-scale characteristics of human observing images and the continuity characteristics of human observing adjacent regions, so a multi-scale recognition network containing both fine-grained and coarse-grained information extraction branches is constructed. Then, considering fine-grained information contains more details and there are similarities and differences between fine-grained and coarse-grained information, fine-grained attention module is further employed to realize the guide of the fine-grained branch to the coarse-grained branch. Among them, the fine-grained information is the key information retained after filtering out the interference information by the Interference Information Elimination (IIE) module. Finally, the key information related to pedestrian ReID is obtained by bivariate difference, and the prediction of pedestrian identity is realized by multi-dimensional joint supervision such as tags and features. Extensive experiments on several public pedestrian ReID databases prove the superiority of this algorithm and the effectiveness and necessity of each module.
Available online , doi: 10.11999/JEIT231226
Abstract:
Sparse Code Multiple Access (SCMA) technology is a highly valued code domain-based Non-Orthogonal Multiple Access (NOMA) technology. In order to solve the problem that the existing SCMA codebook design fails to combine the properties of data and decoder and the high complexity of MPA, a compressed sensing-assisted low-complexity SCMA system optimization design scheme is proposed. First, a codebook self-updating method is designed based on the system bit error rate optimization goal, which uses the gradient descent method to achieve self-updating of the codebook during the sparse vector reconstruction training process. Second, a compressed sensing-assisted multi-user detection algorithm is designed: Sign Decision Orthogonal Matching Pursuit (SD-OMP) algorithm. By sparse processing of the transmitted signal at the transmitting end, compressed sensing technology is used at the receiving end to efficiently detect and reconstruct multi-user sparse signals, this results in a reduction of conflicts between users and a reduction in system complexity. The simulation results show that under Gaussian channel conditions, the compressed sensing-assisted low-complexity SCMA system optimization and design scheme can effectively reduce the complexity of multi-user detection, and can show better bit error rate performance when the system user part is active.
Sparse Code Multiple Access (SCMA) technology is a highly valued code domain-based Non-Orthogonal Multiple Access (NOMA) technology. In order to solve the problem that the existing SCMA codebook design fails to combine the properties of data and decoder and the high complexity of MPA, a compressed sensing-assisted low-complexity SCMA system optimization design scheme is proposed. First, a codebook self-updating method is designed based on the system bit error rate optimization goal, which uses the gradient descent method to achieve self-updating of the codebook during the sparse vector reconstruction training process. Second, a compressed sensing-assisted multi-user detection algorithm is designed: Sign Decision Orthogonal Matching Pursuit (SD-OMP) algorithm. By sparse processing of the transmitted signal at the transmitting end, compressed sensing technology is used at the receiving end to efficiently detect and reconstruct multi-user sparse signals, this results in a reduction of conflicts between users and a reduction in system complexity. The simulation results show that under Gaussian channel conditions, the compressed sensing-assisted low-complexity SCMA system optimization and design scheme can effectively reduce the complexity of multi-user detection, and can show better bit error rate performance when the system user part is active.
Available online , doi: 10.11999/JEIT230999
Abstract:
In the radar target adaptive detection problem, the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix (CCM), which greatly affects the target detection performance. In order to solve this problem, a clutter edge detection method is proposed, which can adaptively discriminate the number and position of clutter edges in auxiliary data. Firstly, assuming the presence of clutter edges in the auxiliary data, the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation, and the clutter edge position is obtained by the cyclic search method. Then, the clutter parameter estimation results are applied to the detection algorithm, and the existence of clutter edges is determined by the generalized likelihood ratio test method. In addition, in order to further improve the robustness of the algorithm under the condition of small samples, the special structure of CCM is introduced as a priori knowledge, and the algorithm is generalized to the situation where CCM is persymmetry, spectrum symmetry and central-symmetry. Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data, and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.
In the radar target adaptive detection problem, the presence of clutter edges in the auxiliary data will cause a serious decrease in the estimation performance of the Clutter Covariance Matrix (CCM), which greatly affects the target detection performance. In order to solve this problem, a clutter edge detection method is proposed, which can adaptively discriminate the number and position of clutter edges in auxiliary data. Firstly, assuming the presence of clutter edges in the auxiliary data, the model order selection algorithm and the maximum likelihood estimation method are used to complete the clutter parameter estimation, and the clutter edge position is obtained by the cyclic search method. Then, the clutter parameter estimation results are applied to the detection algorithm, and the existence of clutter edges is determined by the generalized likelihood ratio test method. In addition, in order to further improve the robustness of the algorithm under the condition of small samples, the special structure of CCM is introduced as a priori knowledge, and the algorithm is generalized to the situation where CCM is persymmetry, spectrum symmetry and central-symmetry. Both simulation and measured data show that the proposed algorithm can efficiently identify the number and location of clutter edges in radar auxiliary data, and the introduction of prior knowledge can further improve the performance of the algorithm when the amount of auxiliary data is small.
Available online , doi: 10.11999/JEIT231123
Abstract:
Two-Dimensional (2-D) convolution with different kernel sizes enriches the overall performance in computer vision tasks. Currently, there is a lack of an efficient design method of reconfigurable 2-D convolver, which limits the deployment of Convolution Neural Network (CNN) models at the edge. In this paper, a new approach based on multiplication management and triangular numbers decomposition is proposed. The proposed 2-D convolver includes a certain number of Processing Elements (PE) and corresponding control units, where the former is responsible for computing tasks and the latter manages the combination of multiplication operations to achieve different convolution sizes. Specifically, an odd number list is determined based on the application scenario, which represents the supported sizes of the 2-D convolutional kernel. The corresponding triangular number list is obtained using the triangular numbers decomposition method. Then, the total number of PEs is determined based on the triangular number list and computational requirements. Finally, the corresponding control units and the interconnection of PEs are determined by the addition combinations of triangular numbers. The proposed reconfigurable 2-D convolver is designed by Verilog Hardware Description Language (HDL) and implemented by Vivado 2022.2 software on the XCZU7EG board. Compared with similar methods, the proposed 2-D convolver significantly improves the efficiency of multiplication resources, increasing from 20%～50% to 89%, and achieves a throughput of 1 500 MB/s with 514 logic units, thereby demonstrating its wide applicability.
Two-Dimensional (2-D) convolution with different kernel sizes enriches the overall performance in computer vision tasks. Currently, there is a lack of an efficient design method of reconfigurable 2-D convolver, which limits the deployment of Convolution Neural Network (CNN) models at the edge. In this paper, a new approach based on multiplication management and triangular numbers decomposition is proposed. The proposed 2-D convolver includes a certain number of Processing Elements (PE) and corresponding control units, where the former is responsible for computing tasks and the latter manages the combination of multiplication operations to achieve different convolution sizes. Specifically, an odd number list is determined based on the application scenario, which represents the supported sizes of the 2-D convolutional kernel. The corresponding triangular number list is obtained using the triangular numbers decomposition method. Then, the total number of PEs is determined based on the triangular number list and computational requirements. Finally, the corresponding control units and the interconnection of PEs are determined by the addition combinations of triangular numbers. The proposed reconfigurable 2-D convolver is designed by Verilog Hardware Description Language (HDL) and implemented by Vivado 2022.2 software on the XCZU7EG board. Compared with similar methods, the proposed 2-D convolver significantly improves the efficiency of multiplication resources, increasing from 20%～50% to 89%, and achieves a throughput of 1 500 MB/s with 514 logic units, thereby demonstrating its wide applicability.
Available online , doi: 10.11999/JEIT231166
Abstract:
True Random Number Generator (TRNG), as an important hardware security primitive, is used in key generation, initialization vector and identity authentication in protocols. In order to design a lightweight TRNG with high throughput, the method of generating metastability is studied by using the switching characteristics of MUltipleXer(MUX) and XOR gate, and a TRNG design based on Metastability Superposition(MS-TRNG) cell (MS-cell) is proposed. It superimposes MUX and XOR gate guided metastases, thereby increasing the entropy of TRNG. The proposed TRNG is implemented in Xilinx Virtex-7 and Xilinx Artix-7 FPGA development boards, respectively, without the need for post-processing circuits. Compared to other advanced TRNGS, the proposed TRNG has the highest throughput and extremely low hardware overhead, and the random sequences it generates pass NIST testing and a series of performance tests.
True Random Number Generator (TRNG), as an important hardware security primitive, is used in key generation, initialization vector and identity authentication in protocols. In order to design a lightweight TRNG with high throughput, the method of generating metastability is studied by using the switching characteristics of MUltipleXer(MUX) and XOR gate, and a TRNG design based on Metastability Superposition(MS-TRNG) cell (MS-cell) is proposed. It superimposes MUX and XOR gate guided metastases, thereby increasing the entropy of TRNG. The proposed TRNG is implemented in Xilinx Virtex-7 and Xilinx Artix-7 FPGA development boards, respectively, without the need for post-processing circuits. Compared to other advanced TRNGS, the proposed TRNG has the highest throughput and extremely low hardware overhead, and the random sequences it generates pass NIST testing and a series of performance tests.
Available online , doi: 10.11999/JEIT230898
Abstract:
Considering the problem of unknown and highly heterogeneous user preference in the current edge caching scenario, a joint optimization strategy of user request perceived edge caching and user recommendation is proposed. Firstly, the basic model of Click Through Rate (CTR) prediction is established, and the contrastive learning method is introduced to generate high-quality feature representation, which could better help Factorization Machine(FM) model to predict user preference. Then, based on the predicted user preference, a dynamic recommendation mechanism is designed to reshape the content request probability of different users, thereby affecting cache decision; Finally, a joint optimization problem of edge caching and user recommendation is established with the goal of minimizing the average user content acquisition delay. It is decoupled into edge caching subproblem and user recommendation subproblem, and solved based on the region greedy algorithm and one-to-one exchange matching algorithm, respectively. The convergence optimization results are obtained through iterative update. The results show that compared with the benchmark model, the contrastive learning method has improved Area Under Curve (AUC) and ACCuracy (ACC) by 1.65% and 1.30%, respectively, and the joint optimization algorithm can effectively reduce the average content acquisition latency of users and improve the system cache performance.
Considering the problem of unknown and highly heterogeneous user preference in the current edge caching scenario, a joint optimization strategy of user request perceived edge caching and user recommendation is proposed. Firstly, the basic model of Click Through Rate (CTR) prediction is established, and the contrastive learning method is introduced to generate high-quality feature representation, which could better help Factorization Machine(FM) model to predict user preference. Then, based on the predicted user preference, a dynamic recommendation mechanism is designed to reshape the content request probability of different users, thereby affecting cache decision; Finally, a joint optimization problem of edge caching and user recommendation is established with the goal of minimizing the average user content acquisition delay. It is decoupled into edge caching subproblem and user recommendation subproblem, and solved based on the region greedy algorithm and one-to-one exchange matching algorithm, respectively. The convergence optimization results are obtained through iterative update. The results show that compared with the benchmark model, the contrastive learning method has improved Area Under Curve (AUC) and ACCuracy (ACC) by 1.65% and 1.30%, respectively, and the joint optimization algorithm can effectively reduce the average content acquisition latency of users and improve the system cache performance.
Available online , doi: 10.11999/JEIT230700
Abstract:
In recent years, time-modulated array has aroused much attention due to its superior performance on vector control. Based on the time modulation method, a type of reconfigurable backscattering communication system based on time modulation technique is proposed in this paper. In backscattering node of the proposed system, multiple digital modulation symbols are mapped into the harmonic component of the control waveforms. The scattering or absorbing states of the incoming wave from the base station are then controlled by the designed waveforms. After the receiver samples the backscattering signal and extracts the control waveforms, the digital modulation symbols transmitted from the backscattering node can be recovered from the harmonic component with the Fourier transform. Simulation results demonstrate the performance of the harmonic demodulation methods and consistency with the theoretical values. Meanwhile, the reconfigurable backscattering transmitting experiments based on amplitude, phase shift keying and quadrature amplitude modulation demonstrate the feasibility of the proposed system and methods. In comparison, the proposed system has the characteristics of low power consumption, simple structure and reconfigurable digital modulation.
In recent years, time-modulated array has aroused much attention due to its superior performance on vector control. Based on the time modulation method, a type of reconfigurable backscattering communication system based on time modulation technique is proposed in this paper. In backscattering node of the proposed system, multiple digital modulation symbols are mapped into the harmonic component of the control waveforms. The scattering or absorbing states of the incoming wave from the base station are then controlled by the designed waveforms. After the receiver samples the backscattering signal and extracts the control waveforms, the digital modulation symbols transmitted from the backscattering node can be recovered from the harmonic component with the Fourier transform. Simulation results demonstrate the performance of the harmonic demodulation methods and consistency with the theoretical values. Meanwhile, the reconfigurable backscattering transmitting experiments based on amplitude, phase shift keying and quadrature amplitude modulation demonstrate the feasibility of the proposed system and methods. In comparison, the proposed system has the characteristics of low power consumption, simple structure and reconfigurable digital modulation.
Available online , doi: 10.11999/JEIT231224
Abstract:
Considering the integrated air-to-ground access network, based on summarizing the relevant research, the key technologies of future air-to-ground integrated access architecture are elaborated, and the research progress in several key directions, such as air-port technology, multiple-access technology, interference analysis, computation technology, and Artificial Intelligence (AI) technology are analyzed, and a flexible network architecture with the coexistence of multiple access forms is proposed. Considering the key research problems of the access architecture in the current air-to-ground integrated network, an integrated AI-enabled architecture is constructed by combining the user’s quality of service demand, and the large-scale hybrid multiple access and flexible resource adaptation strategy are proposed. Based on the future key research directions of network architecture stereoscopic, network cooperative transmission, integrated network resource management, future air-to-ground access technology, and network cooperative computation are discussed and outlooked.
Considering the integrated air-to-ground access network, based on summarizing the relevant research, the key technologies of future air-to-ground integrated access architecture are elaborated, and the research progress in several key directions, such as air-port technology, multiple-access technology, interference analysis, computation technology, and Artificial Intelligence (AI) technology are analyzed, and a flexible network architecture with the coexistence of multiple access forms is proposed. Considering the key research problems of the access architecture in the current air-to-ground integrated network, an integrated AI-enabled architecture is constructed by combining the user’s quality of service demand, and the large-scale hybrid multiple access and flexible resource adaptation strategy are proposed. Based on the future key research directions of network architecture stereoscopic, network cooperative transmission, integrated network resource management, future air-to-ground access technology, and network cooperative computation are discussed and outlooked.
Available online , doi: 10.11999/JEIT231139
Abstract:
For Semi-Coprime Arrays (SCA), the performance of classical Direction of Arrival (DoA) estimation algorithm degrades under the presence of coherent adjacent sources. To address this problem, a high-precison DoA estimation method for SCA is proposed. Firstly, the array is divided into three subarrays (Subarray 1 to 3 respectively). And conventional beamforming algorithm is applied to obtain the signals of the three subarrays, respectively. Then, the output signal of subarray 3 is weighted and added to the output signals of subarray 1 and 2 to construct one sum beam. Meanwhile, the difference between output signals of subarray 1 and subarray 2 is used to construct one difference beam. Finally, the final output signal is obtained by the sum beam signal and the difference beam signal. The azimuth spectrum is the power of the final output signal. This method is based on the characteristic of SCA arrays to construct sum beam and difference beam, fully utilizing the overlapping sensors of the three subarrays to improve estimation accuracy. Simulations and lake experiments are implemented to validate the effectiveness for the proposed method used for DoA estimation in SCA. The proposed method performs better than the existing approaches, such as Minimum Variance Distortionless Response (MVDR) and Min Processing (MP) when facing adjacent coherent sources.
For Semi-Coprime Arrays (SCA), the performance of classical Direction of Arrival (DoA) estimation algorithm degrades under the presence of coherent adjacent sources. To address this problem, a high-precison DoA estimation method for SCA is proposed. Firstly, the array is divided into three subarrays (Subarray 1 to 3 respectively). And conventional beamforming algorithm is applied to obtain the signals of the three subarrays, respectively. Then, the output signal of subarray 3 is weighted and added to the output signals of subarray 1 and 2 to construct one sum beam. Meanwhile, the difference between output signals of subarray 1 and subarray 2 is used to construct one difference beam. Finally, the final output signal is obtained by the sum beam signal and the difference beam signal. The azimuth spectrum is the power of the final output signal. This method is based on the characteristic of SCA arrays to construct sum beam and difference beam, fully utilizing the overlapping sensors of the three subarrays to improve estimation accuracy. Simulations and lake experiments are implemented to validate the effectiveness for the proposed method used for DoA estimation in SCA. The proposed method performs better than the existing approaches, such as Minimum Variance Distortionless Response (MVDR) and Min Processing (MP) when facing adjacent coherent sources.
Available online , doi: 10.11999/JEIT231016
Abstract:
The Space-Air-Ground Integrated Network (SAGIN) can effectively meet the communication needs of various service types by improving the resource utilization of the ground network, but ignoring the adaptive ability and robustness of the system and the Quality of Service (QoS) in different users. In response to this problem, a Deep Reinforcement Learning (DRL) Resource allocation algorithm for urban and suburban communications under the SAGIN architecture is proposed in this paper. Based on Reference Signal Reception Power (RSRP) defined in the 3rd Generation Partnership Project (3GPP) standard, considering ground co-frequency interference, and using the time-frequency resources of base stations in different domains as constraints, an optimization problem to maxmize the downlink throughput of system users is constructed. When using the Deep Q-network (DQN) algorithm to solve the optimization problem, a reward function which can comprehensively consider the user’s QoS requirements, system adaptability and system robustness is defined. Considering the service requirements of unmanned vehicles, immersive services and ordinary mobile communication services, the simulation results show that the value of the reward function which represents the performance of the system is increased by 39.1% compared with the greedy algorithm under 2 000 iterations. For the unmanned vehicle services, the average packet loss rate by the DQN algorithm is 38.07% lower than that by the greedy algorithm, and the delay by the DQN algorithm is also 6.05% lower than that by the greedy algorithm.
The Space-Air-Ground Integrated Network (SAGIN) can effectively meet the communication needs of various service types by improving the resource utilization of the ground network, but ignoring the adaptive ability and robustness of the system and the Quality of Service (QoS) in different users. In response to this problem, a Deep Reinforcement Learning (DRL) Resource allocation algorithm for urban and suburban communications under the SAGIN architecture is proposed in this paper. Based on Reference Signal Reception Power (RSRP) defined in the 3rd Generation Partnership Project (3GPP) standard, considering ground co-frequency interference, and using the time-frequency resources of base stations in different domains as constraints, an optimization problem to maxmize the downlink throughput of system users is constructed. When using the Deep Q-network (DQN) algorithm to solve the optimization problem, a reward function which can comprehensively consider the user’s QoS requirements, system adaptability and system robustness is defined. Considering the service requirements of unmanned vehicles, immersive services and ordinary mobile communication services, the simulation results show that the value of the reward function which represents the performance of the system is increased by 39.1% compared with the greedy algorithm under 2 000 iterations. For the unmanned vehicle services, the average packet loss rate by the DQN algorithm is 38.07% lower than that by the greedy algorithm, and the delay by the DQN algorithm is also 6.05% lower than that by the greedy algorithm.
Available online , doi: 10.11999/JEIT231142
Abstract:
With the drastic increase in the number of users, the existing wireless resources have become unsustainable. Therefore, the reactivation of Power Line Communication (PLC) has attracted the attention of major research institutes and industries. The development of PLC has been slow due to the complexity of the channel environment and the complexity and high cost of existing processing solutions. The most extensive work has been done on impulse noise, and it is particularly important to achieve robustness of data transmission against impulse noise at low cost. Firstly, the mainstream noise in PLC environment and its classification are introduced in this paper, and then the Differential Chaos Shift Keying (DCSK) and M-ary DCSK (MDCSK) modulation techniques with low cost and low complexity are described. The characteristics of this system in PLCs are presented and analysed, as well as the advantages and improvements that exist for various types of impulse noise. Secondly, some relevant new coding and modulation schemes are introduced in order to improve the transmission quality in band-limited environments. The results show that these optimisations significantly improve the system performance. Subsequently, modulation and coded modulation transmission optimisation schemes for PLC overall channel characteristics system parameters will be a hot topic for future work.
With the drastic increase in the number of users, the existing wireless resources have become unsustainable. Therefore, the reactivation of Power Line Communication (PLC) has attracted the attention of major research institutes and industries. The development of PLC has been slow due to the complexity of the channel environment and the complexity and high cost of existing processing solutions. The most extensive work has been done on impulse noise, and it is particularly important to achieve robustness of data transmission against impulse noise at low cost. Firstly, the mainstream noise in PLC environment and its classification are introduced in this paper, and then the Differential Chaos Shift Keying (DCSK) and M-ary DCSK (MDCSK) modulation techniques with low cost and low complexity are described. The characteristics of this system in PLCs are presented and analysed, as well as the advantages and improvements that exist for various types of impulse noise. Secondly, some relevant new coding and modulation schemes are introduced in order to improve the transmission quality in band-limited environments. The results show that these optimisations significantly improve the system performance. Subsequently, modulation and coded modulation transmission optimisation schemes for PLC overall channel characteristics system parameters will be a hot topic for future work.
Available online , doi: 10.11999/JEIT231115
Abstract:
The short packet covert communication using a shared-aperture jammer assisted Time-Modulated Array (TMA) is investigated for the first time in this paper. Firstly, a TMA architecture for shared-aperture jammer is proposed and an optimization method is introduced that maximizes the gain of the target direction while forming interference in non-target directions. Based on this model, closed-form expressions for the covertness constraint and covert throughput are derived. Furthermore, the transmission power and blocklength are optimized to maximize the covert throughput. Simulation results show that there exists an optimum blocklength that maximizes the covert throughput, and the proposed scheme outperforms the benchmark scheme in terms of covert communication performance.
The short packet covert communication using a shared-aperture jammer assisted Time-Modulated Array (TMA) is investigated for the first time in this paper. Firstly, a TMA architecture for shared-aperture jammer is proposed and an optimization method is introduced that maximizes the gain of the target direction while forming interference in non-target directions. Based on this model, closed-form expressions for the covertness constraint and covert throughput are derived. Furthermore, the transmission power and blocklength are optimized to maximize the covert throughput. Simulation results show that there exists an optimum blocklength that maximizes the covert throughput, and the proposed scheme outperforms the benchmark scheme in terms of covert communication performance.
Available online , doi: 10.11999/JEIT230692
Abstract:
Combining Intelligent Reflective Surface (IRS) with massive MIMO can guarantee and improve the performance of millimeter-wave communication systems. An adaptive full-channel estimation method is proposed for the Base Station (BS)-user direct-connect channel and user-IRS-BS reflective channel mixing scenario. First, auxiliary variables are introduced and atomic paradigms are used to correlate the sparse angle-domain subspaces of the direct-connect and reflective channels; then, the full-channel estimation problem is modeled as a continuous angle-domain sparse matrix reconstruction planning by using atomic paradigm minimization; and finally, a low-complexity problem solving algorithm based on the immovable-point deep learning network is designed. The algorithm can not only overcome the dependence of the nonlinear estimation operator on a priori knowledge in the traditional model-based solution method but also adaptively adjust the complexity of the algorithm according to the changes of the mobile scene. Simulation results show that the proposed algorithm can avoid the error propagation effect caused by the traditional time-division estimation strategy, and has higher estimation accuracy and lower complexity.
Combining Intelligent Reflective Surface (IRS) with massive MIMO can guarantee and improve the performance of millimeter-wave communication systems. An adaptive full-channel estimation method is proposed for the Base Station (BS)-user direct-connect channel and user-IRS-BS reflective channel mixing scenario. First, auxiliary variables are introduced and atomic paradigms are used to correlate the sparse angle-domain subspaces of the direct-connect and reflective channels; then, the full-channel estimation problem is modeled as a continuous angle-domain sparse matrix reconstruction planning by using atomic paradigm minimization; and finally, a low-complexity problem solving algorithm based on the immovable-point deep learning network is designed. The algorithm can not only overcome the dependence of the nonlinear estimation operator on a priori knowledge in the traditional model-based solution method but also adaptively adjust the complexity of the algorithm according to the changes of the mobile scene. Simulation results show that the proposed algorithm can avoid the error propagation effect caused by the traditional time-division estimation strategy, and has higher estimation accuracy and lower complexity.
Available online , doi: 10.11999/JEIT230708
Abstract:
An intelligent semantic location privacy protection method based on 3D Geo-Indistinguishability (3D-GI) is studied for the privacy leakage problem of sensitive semantic locations (such as medicine stores and bookstores) in 3D space location-based services, such as hospitals and shopping centers. Reinforcement Learning (RL) techniques are used in this paper to optimize user’s semantic location privacy protection policies dynamically. Specifically, a 3D semantic location perturbation mechanism is proposed based on the Policy Hill Climbing (PHC) algorithm, independent of specific environments and attack models. This mechanism induces attackers to infer less sensitive locations to reduce the exposure of sensitive semantic locations. To address the dimensional disaster problem of complex 3D space, a 3D semantic location perturbation mechanism based on the Proximal Policy Optimization (PPO) algorithm is further proposed. This mechanism captures the environment features using a neural network and optimizes the neural network parameter updates through the offline policy gradient method to improve the efficiency of semantic location perturbation policy selection. Experimental results show that the proposed mechanism improves both semantic location privacy protection and user service experience.
An intelligent semantic location privacy protection method based on 3D Geo-Indistinguishability (3D-GI) is studied for the privacy leakage problem of sensitive semantic locations (such as medicine stores and bookstores) in 3D space location-based services, such as hospitals and shopping centers. Reinforcement Learning (RL) techniques are used in this paper to optimize user’s semantic location privacy protection policies dynamically. Specifically, a 3D semantic location perturbation mechanism is proposed based on the Policy Hill Climbing (PHC) algorithm, independent of specific environments and attack models. This mechanism induces attackers to infer less sensitive locations to reduce the exposure of sensitive semantic locations. To address the dimensional disaster problem of complex 3D space, a 3D semantic location perturbation mechanism based on the Proximal Policy Optimization (PPO) algorithm is further proposed. This mechanism captures the environment features using a neural network and optimizes the neural network parameter updates through the offline policy gradient method to improve the efficiency of semantic location perturbation policy selection. Experimental results show that the proposed mechanism improves both semantic location privacy protection and user service experience.
Available online , doi: 10.11999/JEIT230918
Abstract:
In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles (IoV), a Two-layer Asynchronous Federated Learning with Two-factor updating (TTAFL) framework is established in this paper. Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning (FL) on server model updates, a model update scheme based on staleness factor and contribution factor is proposed. Furthermore, to avoid the problem of roadside unit switching caused by vehicle mobility during the training process, a node selection scheme considering the residence time is given. Finally, in order to reduce the accuracy loss and system energy consumption, a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units. Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.
In response to the characteristics of heterogeneous node resources and dynamic changes in the network topology in the Internet of Vehicles (IoV), a Two-layer Asynchronous Federated Learning with Two-factor updating (TTAFL) framework is established in this paper. Considering the impact of model version differences and the number of times that vehicles participate in Federated Learning (FL) on server model updates, a model update scheme based on staleness factor and contribution factor is proposed. Furthermore, to avoid the problem of roadside unit switching caused by vehicle mobility during the training process, a node selection scheme considering the residence time is given. Finally, in order to reduce the accuracy loss and system energy consumption, a reinforcement learning method is used to optimize the number of local iterations of FL and the number of local model updates of roadside units. Simulation results show that the proposed algorithm effectively improves the training efficiency and training accuracy of federated learning and reduces the system energy consumption.
Available online , doi: 10.11999/JEIT230656
Abstract:
To solve the problems of long runtime, low spectral rate and high bit error rate, which exist in conventional hybrid beamforming schemes, a hybrid beamforming algorithm based on Limited-Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is proposed. Firstly, a single variable objective function is constructed through the least squares solution of the digital precoder. Then, the gradient of the objective function is used to approximate the inverse of the Hessian matrix for obtaining the search direction and the analog precoder is updated along the search direction until the stop condition is satisfied. Finally, the analog precoder is fixed to obtain the digital precoder. The MATLAB simulation analysis indicate that LBFGS algorithm reduces the running time by 28%, increases spectral rate by 1.05%, and reduces bit error rate by 1.06%, compared to MO algorithm.
To solve the problems of long runtime, low spectral rate and high bit error rate, which exist in conventional hybrid beamforming schemes, a hybrid beamforming algorithm based on Limited-Broyden-Fletcher-Goldfarb-Shanno (LBFGS) is proposed. Firstly, a single variable objective function is constructed through the least squares solution of the digital precoder. Then, the gradient of the objective function is used to approximate the inverse of the Hessian matrix for obtaining the search direction and the analog precoder is updated along the search direction until the stop condition is satisfied. Finally, the analog precoder is fixed to obtain the digital precoder. The MATLAB simulation analysis indicate that LBFGS algorithm reduces the running time by 28%, increases spectral rate by 1.05%, and reduces bit error rate by 1.06%, compared to MO algorithm.
Available online , doi: 10.11999/JEIT230644
Abstract:
Considering the electromagnetic spectrum congestion and serious interference, the Free-Space Optical (FSO)-based Unmanned Aerial Vehicle (UAV) communication network constitutes an important part for the space-air-ground integration, attracting substantial attention from both academia and industry. Compared to radio frequency communication, FSO communication is benefited from high data rate, low latency and high security. However, the FSO link is susceptible to atmospheric environment, while the mobile UAV dynamics topology and limited resources bring further challenges. Therefore, this paper first introduces the FSO transmission characteristics and then focuses on the key technologies to enhance stability and quality of FSO-based UAV networks. Furthermore, the development trend of FSO-based UAV network, in terms of high reliability, strong intelligence and long endurance is analyzed.
Considering the electromagnetic spectrum congestion and serious interference, the Free-Space Optical (FSO)-based Unmanned Aerial Vehicle (UAV) communication network constitutes an important part for the space-air-ground integration, attracting substantial attention from both academia and industry. Compared to radio frequency communication, FSO communication is benefited from high data rate, low latency and high security. However, the FSO link is susceptible to atmospheric environment, while the mobile UAV dynamics topology and limited resources bring further challenges. Therefore, this paper first introduces the FSO transmission characteristics and then focuses on the key technologies to enhance stability and quality of FSO-based UAV networks. Furthermore, the development trend of FSO-based UAV network, in terms of high reliability, strong intelligence and long endurance is analyzed.
Available online , doi: 10.11999/JEIT230655
Abstract:
E2 is one of the 15 candidate algorithms in the first round of AES, which has the characteristics of excellent software and hardware implementation efficiency and strong security. The meet-in-the-middle attacks on E2 are carried out in this paper by using multiset tabulation technique and differential enumeration technique. First, E2-128 is taken as an example to improve the existing 4-round meet-in-the-middle distinguisher, and the pre-computation complexity of 5-round key recovery attack is reduced to\begin{document}${2^{31}}$\end{document} \begin{document}${2^{105}}$\end{document} \begin{document}${2^{200}}$\end{document} \begin{document}${2^{205}}$\end{document}
E2 is one of the 15 candidate algorithms in the first round of AES, which has the characteristics of excellent software and hardware implementation efficiency and strong security. The meet-in-the-middle attacks on E2 are carried out in this paper by using multiset tabulation technique and differential enumeration technique. First, E2-128 is taken as an example to improve the existing 4-round meet-in-the-middle distinguisher, and the pre-computation complexity of 5-round key recovery attack is reduced to
Available online , doi: 10.11999/JEIT231067
Abstract:
The integrated scheduling algorithm of flexible equipment network is difficult to reasonably select the relevant processes of processing equipment, which affects the completion time of products. An Integrated Scheduling algorithm for Flexible Equipment Network considering the Same layer after Process (SP-FENIS) is proposed. Firstly, the priority strategy of the reverse order layer is adopted, which assigns each process to the set of processes to be scheduled in the reverse layer. Then, the average reverse-order compact path strategy is proposed to determine the scheduling sequence of the processes to be scheduled in each reverse order layer. Finally, the earliest completion time strategy and equipment idle insertion strategy are proposed. When the earliest completion time of the process on the flexible equipment is the same, the processing time on the flexible equipment and the processing equipment of the same layer after the process are considered, and the processing equipment and processing time of the target process are determined. The example shows that, compared with the existing algorithm, the proposed algorithm can shorten the product completion time.
The integrated scheduling algorithm of flexible equipment network is difficult to reasonably select the relevant processes of processing equipment, which affects the completion time of products. An Integrated Scheduling algorithm for Flexible Equipment Network considering the Same layer after Process (SP-FENIS) is proposed. Firstly, the priority strategy of the reverse order layer is adopted, which assigns each process to the set of processes to be scheduled in the reverse layer. Then, the average reverse-order compact path strategy is proposed to determine the scheduling sequence of the processes to be scheduled in each reverse order layer. Finally, the earliest completion time strategy and equipment idle insertion strategy are proposed. When the earliest completion time of the process on the flexible equipment is the same, the processing time on the flexible equipment and the processing equipment of the same layer after the process are considered, and the processing equipment and processing time of the target process are determined. The example shows that, compared with the existing algorithm, the proposed algorithm can shorten the product completion time.
Available online , doi: 10.11999/JEIT230825
Abstract:
Considering the problem that the performance of the traditional abnormal traffic detection models is limited by the low utilization of spatiotemporal features of traffic data, an abnormal traffic detection method MSECNN-BiLSTM based on the combination of Convolutional Neural Network (CNN), Multi head Squeeze Excitation mechanism (MSE), and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. The one-dimensional CNN is used to capture abnormal traffic features at spatial scales. The MSE mechanism is introduced to adaptively calibrate the feature weights and strengthen the model's ability to correlate global features from multiple perspectives. The traffic features are input into BiLSTM to capture the temporal dependencies of the traffic data and further model the relationship of network traffic on the time scale. The softmax classifier is employed for traffic detection. The experimental results verify that the proposed model is effective in the field of abnormal traffic detection.
Considering the problem that the performance of the traditional abnormal traffic detection models is limited by the low utilization of spatiotemporal features of traffic data, an abnormal traffic detection method MSECNN-BiLSTM based on the combination of Convolutional Neural Network (CNN), Multi head Squeeze Excitation mechanism (MSE), and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. The one-dimensional CNN is used to capture abnormal traffic features at spatial scales. The MSE mechanism is introduced to adaptively calibrate the feature weights and strengthen the model's ability to correlate global features from multiple perspectives. The traffic features are input into BiLSTM to capture the temporal dependencies of the traffic data and further model the relationship of network traffic on the time scale. The softmax classifier is employed for traffic detection. The experimental results verify that the proposed model is effective in the field of abnormal traffic detection.
Available online , doi: 10.11999/JEIT230678
Abstract:
Focusing on the problems of difficult cooperation between vehicles, low quality of the model trained by cooperation and poor effect of direct application of the obtained results to physical vehicles in the process of path planning for multiple Autonomous Vehicles (AVs), a distributed collaborative path planning algorithm is proposed for multiple AVs based on Digital Twin (DT). The algorithm is based on the Credibility-Weighted Decentralized Federated Reinforcement Learning (CWDFRL) to realize the path planning of multiple AVs. In this paper, the path planning problem of a single AVs is first modeled as the problem of minimizing the average task completion time under the constraints of driving behavior, which is transformed into Markov Decision Process (MDP) and solved by Deep Deterministic Policy Gradient algorithm (DDPG). Then Federated Learning (FL) is used to ensure the cooperation between vehicles. Aiming at the problem of low quality of global model update in centralized FL, this paper uses a decentralized FL training method based on dynamic node selection of reliability to improve the low quality. Finally, the DT is used to assist the training of the Decentralized Federated Reinforcement Learning (DFRL) model, and the trained model can be quickly deployed directly to the real-world AVs by taking advantage of the twin's ability of learning from DT environment. The simulation results show that compared with the existing methods, the proposed training framework can obtain a higher reward, effectively improve the utilization of the vehicle’s own speed, and at the same time reduce the average task completion time and collision probability of the vehicle swarm.
Focusing on the problems of difficult cooperation between vehicles, low quality of the model trained by cooperation and poor effect of direct application of the obtained results to physical vehicles in the process of path planning for multiple Autonomous Vehicles (AVs), a distributed collaborative path planning algorithm is proposed for multiple AVs based on Digital Twin (DT). The algorithm is based on the Credibility-Weighted Decentralized Federated Reinforcement Learning (CWDFRL) to realize the path planning of multiple AVs. In this paper, the path planning problem of a single AVs is first modeled as the problem of minimizing the average task completion time under the constraints of driving behavior, which is transformed into Markov Decision Process (MDP) and solved by Deep Deterministic Policy Gradient algorithm (DDPG). Then Federated Learning (FL) is used to ensure the cooperation between vehicles. Aiming at the problem of low quality of global model update in centralized FL, this paper uses a decentralized FL training method based on dynamic node selection of reliability to improve the low quality. Finally, the DT is used to assist the training of the Decentralized Federated Reinforcement Learning (DFRL) model, and the trained model can be quickly deployed directly to the real-world AVs by taking advantage of the twin's ability of learning from DT environment. The simulation results show that compared with the existing methods, the proposed training framework can obtain a higher reward, effectively improve the utilization of the vehicle’s own speed, and at the same time reduce the average task completion time and collision probability of the vehicle swarm.
Available online , doi: 10.11999/JEIT230770
Abstract:
The architecture of Mobile Edge Computing (MEC), assisted by Unmanned Aerial Vehicles (UAVs), is an efficient model for flexible management of mobile computing-intensive and delay-sensitive tasks. Nevertheless, achieving an optimal balance between task latency and energy consumption during task processing has been a challenging issue in vehicular communication applications. To tackle this problem, this paper introduces a model for optimizing task offloading and power control in vehicle networks based on UAV-assisted mobile edge computing architecture, using a Non-Orthogonal Multiple Access (NOMA) approach. The proposed model takes into account dynamic factors like vehicle high mobility and wireless channel time-variations. The problem is modeled as a Markov decision process. A distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed, enabling each vehicle to make autonomous decisions on task offloading and related transmission power based on its own perceptual local information. This achieves the optimal balance between task latency and energy consumption. Simulation results reveal that the proposed proximal policy optimization algorithm for task offloading and power control scheme improves not only the performance of task latency and energy consumption compared to existing methods, The average system cost performance improvement is at least 13% or more. but also offers a performance-balanced optimization method. This method achieves optimal balance between the system task latency and energy consumption level by adjusting user preference weight factors.
The architecture of Mobile Edge Computing (MEC), assisted by Unmanned Aerial Vehicles (UAVs), is an efficient model for flexible management of mobile computing-intensive and delay-sensitive tasks. Nevertheless, achieving an optimal balance between task latency and energy consumption during task processing has been a challenging issue in vehicular communication applications. To tackle this problem, this paper introduces a model for optimizing task offloading and power control in vehicle networks based on UAV-assisted mobile edge computing architecture, using a Non-Orthogonal Multiple Access (NOMA) approach. The proposed model takes into account dynamic factors like vehicle high mobility and wireless channel time-variations. The problem is modeled as a Markov decision process. A distributed deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed, enabling each vehicle to make autonomous decisions on task offloading and related transmission power based on its own perceptual local information. This achieves the optimal balance between task latency and energy consumption. Simulation results reveal that the proposed proximal policy optimization algorithm for task offloading and power control scheme improves not only the performance of task latency and energy consumption compared to existing methods, The average system cost performance improvement is at least 13% or more. but also offers a performance-balanced optimization method. This method achieves optimal balance between the system task latency and energy consumption level by adjusting user preference weight factors.
Available online , doi: 10.11999/JEIT230719
Abstract:
Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing fully convolutional networks-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing fully convolutional networks-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
Available online , doi: 10.11999/JEIT230641
Abstract:
In order to improve the service completion rate of mobile nodes and the efficiency of network resource allocation in maritime heterogeneous wireless network, a network access selection algorithm based on Hilbert space vector assignment is proposed to address the problems of poor matching between existing network selection algorithms and service demands, and low service completion rate in dynamic environment. The algorithm adopts the network-service matching model based on Hilbert space, maps the network characteristics and service requirements to the same space, and measures whether the network meets the service requirements in the same coordinate system; at the same time, it adopts the pre-switching network selection algorithm based on the Technique for Order Preference by Similarity to Ideal Solution method, and introduces the network-service matching weights to correct the normalization matrix of the distance-to-preferred-solution method, so as to ensure that the selected network matches the service requirements, and to ensure that the network matches the service requirements. This ensures that the selected network matches the service requirements and overcomes the problems of traditional network selection where the service requirements are less considered and the network characteristics and service requirements are difficult to be measured uniformly. In addition, the network switching control algorithm based on spatial distance is adopted, and matching weight and spatial distance are introduced into the network switching control to ensure the continuity of service transmission and improve the service completion rate in the dynamic environment. Simulation results show that compared with the comparison algorithm, the service completion rate of this algorithm is improved by at least 6.81%, which effectively improves the service transmission capacity and smoothness of the network, and indirectly realizes the effective allocation of network resources.
In order to improve the service completion rate of mobile nodes and the efficiency of network resource allocation in maritime heterogeneous wireless network, a network access selection algorithm based on Hilbert space vector assignment is proposed to address the problems of poor matching between existing network selection algorithms and service demands, and low service completion rate in dynamic environment. The algorithm adopts the network-service matching model based on Hilbert space, maps the network characteristics and service requirements to the same space, and measures whether the network meets the service requirements in the same coordinate system; at the same time, it adopts the pre-switching network selection algorithm based on the Technique for Order Preference by Similarity to Ideal Solution method, and introduces the network-service matching weights to correct the normalization matrix of the distance-to-preferred-solution method, so as to ensure that the selected network matches the service requirements, and to ensure that the network matches the service requirements. This ensures that the selected network matches the service requirements and overcomes the problems of traditional network selection where the service requirements are less considered and the network characteristics and service requirements are difficult to be measured uniformly. In addition, the network switching control algorithm based on spatial distance is adopted, and matching weight and spatial distance are introduced into the network switching control to ensure the continuity of service transmission and improve the service completion rate in the dynamic environment. Simulation results show that compared with the comparison algorithm, the service completion rate of this algorithm is improved by at least 6.81%, which effectively improves the service transmission capacity and smoothness of the network, and indirectly realizes the effective allocation of network resources.
Available online , doi: 10.11999/JEIT230707
Abstract:
Aiming at the problems that the traditional UAV formation routing algorithm cannot effectively utilize the advance predictability of topology changes, and the high cost is caused by acquiring the link connection by sending detection packets, a UAV formation shortest delay routing protocol based on discrete time aggregation graph is proposed by introducing the time-varying graph model. Firstly, using the prior knowledge of the UAV formation network, such as the movement trajectory of nodes and the network topology changes, the network link resources and network topology are characterized by using the discrete time aggregation graph. Secondly, the routing decision algorithm is designed based on the graph model. The delay in the process of route discovery is used as the link weight to solve the shortest delay route from the source node to the destination node of the network. Finally, the simulation performance shows that the routing protocol improves the packet delivery rate, reduces the end-to-end delay and diminishes the network control overhead compared with the traditional Ad-hoc On-Demand Distance Vector routing protocol.
Aiming at the problems that the traditional UAV formation routing algorithm cannot effectively utilize the advance predictability of topology changes, and the high cost is caused by acquiring the link connection by sending detection packets, a UAV formation shortest delay routing protocol based on discrete time aggregation graph is proposed by introducing the time-varying graph model. Firstly, using the prior knowledge of the UAV formation network, such as the movement trajectory of nodes and the network topology changes, the network link resources and network topology are characterized by using the discrete time aggregation graph. Secondly, the routing decision algorithm is designed based on the graph model. The delay in the process of route discovery is used as the link weight to solve the shortest delay route from the source node to the destination node of the network. Finally, the simulation performance shows that the routing protocol improves the packet delivery rate, reduces the end-to-end delay and diminishes the network control overhead compared with the traditional Ad-hoc On-Demand Distance Vector routing protocol.
Available online , doi: 10.11999/JEIT231057
Abstract:
The existing parasitic backscatter communications rely on the direct links between transceivers and do not work when the direct links are blocked or fade deeply. To solve this problem, a relay-assisted parasitic backscatter communication network is proposed, base on which its outage performance is analyzed. Specifically, according to the proposed network, the instantaneous signal-to-noise ratios to decode the primary and secondary systems are given, and then the outage probabilities of primary and secondary systems on the basis of the energy-causality constraint of the secondary user are defined. Under the Rayleigh channel fading model, the expressions for the outage probability of the primary and secondary systems can be obtained by exploiting mathematical theory. Computer simulations validate the accuracy of the derived primary and secondary system outage probabilities, on which the impacts of different system parameters are analyzed.
The existing parasitic backscatter communications rely on the direct links between transceivers and do not work when the direct links are blocked or fade deeply. To solve this problem, a relay-assisted parasitic backscatter communication network is proposed, base on which its outage performance is analyzed. Specifically, according to the proposed network, the instantaneous signal-to-noise ratios to decode the primary and secondary systems are given, and then the outage probabilities of primary and secondary systems on the basis of the energy-causality constraint of the secondary user are defined. Under the Rayleigh channel fading model, the expressions for the outage probability of the primary and secondary systems can be obtained by exploiting mathematical theory. Computer simulations validate the accuracy of the derived primary and secondary system outage probabilities, on which the impacts of different system parameters are analyzed.
Available online , doi: 10.11999/JEIT231140
Abstract:
The future Sixth-Generation (6G) wireless communication systems are required to support ultra-large-scale user demands, with increasing demands for spectrum efficiency and energy efficiency. In this context, holographic Multiple-Input Multiple-Output (MIMO) technology has gained increasing attention due to its potential for intelligent reconfigurability, electromagnetic tunability, high directional gain, cost-effectiveness, and flexible deployment. In holographic MIMO system, large amount small and cheap antenna units are integrated tightly, thus realize high directional gain at a low hardware cost and flexible adjustment of electromagnetic wave at the same time, thereby effectively enhance the performance of wireless communication. A brief introduction to holographic MIMO technology is provided at the start of this paper, covering its current status, development process, classification, and key characteristics. Subsequently, the channel model for holographic MIMO in line-of-sight scenarios and non-line-of-sight scenarios with spatially smooth scattering is presented. Finally, the challenges and future trends faced by holographic MIMO technology are described, and the article is concluded.
The future Sixth-Generation (6G) wireless communication systems are required to support ultra-large-scale user demands, with increasing demands for spectrum efficiency and energy efficiency. In this context, holographic Multiple-Input Multiple-Output (MIMO) technology has gained increasing attention due to its potential for intelligent reconfigurability, electromagnetic tunability, high directional gain, cost-effectiveness, and flexible deployment. In holographic MIMO system, large amount small and cheap antenna units are integrated tightly, thus realize high directional gain at a low hardware cost and flexible adjustment of electromagnetic wave at the same time, thereby effectively enhance the performance of wireless communication. A brief introduction to holographic MIMO technology is provided at the start of this paper, covering its current status, development process, classification, and key characteristics. Subsequently, the channel model for holographic MIMO in line-of-sight scenarios and non-line-of-sight scenarios with spatially smooth scattering is presented. Finally, the challenges and future trends faced by holographic MIMO technology are described, and the article is concluded.
Available online , doi: 10.11999/JEIT230941
Abstract:
Low Earth Orbit (LEO) satellite has the characteristics of low communication delay, low deployment cost and wide coverage, and has become an important part of the construction of the future space earth integrated network. However, satellite communication has large end-to-end propagation distance, complex fading and fast terminal movement speed, thus the channel characteristics are very different from the terrestrial cellular network. Based on this, in order to have a more comprehensive understanding of the characteristics and channel model of LEO satellite-ground channel, the current standardization progress of the satellite-ground channel by the international standards organization are summarized, the fading characteristics of the satellite ground channel at different propagation positions are discussed, the existing important channel models are classified and shown according to the modeling method, and finally the prospects for future work are proposed.
Low Earth Orbit (LEO) satellite has the characteristics of low communication delay, low deployment cost and wide coverage, and has become an important part of the construction of the future space earth integrated network. However, satellite communication has large end-to-end propagation distance, complex fading and fast terminal movement speed, thus the channel characteristics are very different from the terrestrial cellular network. Based on this, in order to have a more comprehensive understanding of the characteristics and channel model of LEO satellite-ground channel, the current standardization progress of the satellite-ground channel by the international standards organization are summarized, the fading characteristics of the satellite ground channel at different propagation positions are discussed, the existing important channel models are classified and shown according to the modeling method, and finally the prospects for future work are proposed.
Available online , doi: 10.11999/JEIT231113
Abstract:
The style transfer method, due to its excellent domain adaptation capability, is widely used to alleviate domain gap of computer vision domain. Currently, stereo matching based on style transfer faces the following challenges: (1) The transformed left and right images need to remain matched; (2) The content and spatial information of the transformed images should remain consistent with the original images. To address these challenges, an Edge Domain Adaptation Stereo matching (EDA-Stereo) method is proposed. First, an Edge-guided Generative Adversarial Network (Edge-GAN) is constructed. by incorporating edge cues and synthetic features through the Spatial Feature Transform (SFT) layer. the Edge-GAN guides the generator to produce pseudo-images that retain the structural features of syntheitic domain images. Second, a warping loss is introduced to guarantee the left image to be reconstructed based on the transformed right image to approximate the original left image, preventing mismatches between the transformed left and right images. Finally, a normal loss based stetreo matching network is proposed to capture more geometric details by characterizing local depth variations, thereby improving matching accuracy. By training on synthetic datasets and comparing with various methods on real datasets, results show the effectiveness in mitigating domain gaps. On the KITTI 2012 and KITTI 2015 datasets, the D1 error is 3.9% and 4.8%, respectively, which is a relative reduction of 37% and 26% compared to the state-of-the-art Domain-invariant Stereo Matching Networks (DSM-Net) method.
The style transfer method, due to its excellent domain adaptation capability, is widely used to alleviate domain gap of computer vision domain. Currently, stereo matching based on style transfer faces the following challenges: (1) The transformed left and right images need to remain matched; (2) The content and spatial information of the transformed images should remain consistent with the original images. To address these challenges, an Edge Domain Adaptation Stereo matching (EDA-Stereo) method is proposed. First, an Edge-guided Generative Adversarial Network (Edge-GAN) is constructed. by incorporating edge cues and synthetic features through the Spatial Feature Transform (SFT) layer. the Edge-GAN guides the generator to produce pseudo-images that retain the structural features of syntheitic domain images. Second, a warping loss is introduced to guarantee the left image to be reconstructed based on the transformed right image to approximate the original left image, preventing mismatches between the transformed left and right images. Finally, a normal loss based stetreo matching network is proposed to capture more geometric details by characterizing local depth variations, thereby improving matching accuracy. By training on synthetic datasets and comparing with various methods on real datasets, results show the effectiveness in mitigating domain gaps. On the KITTI 2012 and KITTI 2015 datasets, the D1 error is 3.9% and 4.8%, respectively, which is a relative reduction of 37% and 26% compared to the state-of-the-art Domain-invariant Stereo Matching Networks (DSM-Net) method.
Available online , doi: 10.11999/JEIT230801
Abstract:
To address the challenge of aligning multimodal data and improving the slow translation speed in sign language translation, a Transformer Sign Language Translation Non-Autoregression (Trans-SLT-NA) is proposed in this paper, which utilizes a self-attention mechanism. Additionally, it incorporates a contrastive learning loss function to align the multimodal data. By capturing the contextual and interaction information between the input sequence (sign language videos) and the target sequence (text), the proposed model is able to perform sign language translation to natural language in s single step. The effectiveness of the proposed model is evaluated on publicly available datasets, including PHOENIX-2014-T (German), CSL (Chinese) and How2Sign (English). Results demonstrate that the proposed method achieves a significant improvement in translation speed, with a speed boost ranging from 11.6 to 17.6 times compared to autoregressive models, while maintaining comparable performance in terms of BiLingual Evaluation Understudy (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.
To address the challenge of aligning multimodal data and improving the slow translation speed in sign language translation, a Transformer Sign Language Translation Non-Autoregression (Trans-SLT-NA) is proposed in this paper, which utilizes a self-attention mechanism. Additionally, it incorporates a contrastive learning loss function to align the multimodal data. By capturing the contextual and interaction information between the input sequence (sign language videos) and the target sequence (text), the proposed model is able to perform sign language translation to natural language in s single step. The effectiveness of the proposed model is evaluated on publicly available datasets, including PHOENIX-2014-T (German), CSL (Chinese) and How2Sign (English). Results demonstrate that the proposed method achieves a significant improvement in translation speed, with a speed boost ranging from 11.6 to 17.6 times compared to autoregressive models, while maintaining comparable performance in terms of BiLingual Evaluation Understudy (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics.
Available online , doi: 10.11999/JEIT231001
Abstract:
The considered Backscatter Communication (BackCom) system consists of one dedicated radio frequency source node, some tags and one destination node. In consideration of the Channel Estimation Error (CEE), the tag selection scheme in which the tag selection scheme that can maximize the received Signal-to-Noise Ratio (SNR) at the destination is proposed over the Nakagami-m channels, and the corresponding analytical results of the outage probability and diversity gain are derived. In this paper, the consumed power by tags is considered. The numerical results verify the obtained analytical results and investigate the key parameters on the system performance. Both the analytical and numerical results show that the existence of the CEE make the corresponding diversity gain zero.
The considered Backscatter Communication (BackCom) system consists of one dedicated radio frequency source node, some tags and one destination node. In consideration of the Channel Estimation Error (CEE), the tag selection scheme in which the tag selection scheme that can maximize the received Signal-to-Noise Ratio (SNR) at the destination is proposed over the Nakagami-m channels, and the corresponding analytical results of the outage probability and diversity gain are derived. In this paper, the consumed power by tags is considered. The numerical results verify the obtained analytical results and investigate the key parameters on the system performance. Both the analytical and numerical results show that the existence of the CEE make the corresponding diversity gain zero.
Available online , doi: 10.11999/JEIT230448
Abstract:
Fully Homomorphic Encryption (FHE) is a multi-party secure computation protocol characterized by its high computational complexity and low interaction requirements. Although there is no need for multiple rounds of interactions and extensive communications between computing participants in protocols based on FHE, the processing of encrypted data is typically\begin{document}$ {10}^{3} $\end{document} \begin{document}$ {10}^{6} $\end{document}
Fully Homomorphic Encryption (FHE) is a multi-party secure computation protocol characterized by its high computational complexity and low interaction requirements. Although there is no need for multiple rounds of interactions and extensive communications between computing participants in protocols based on FHE, the processing of encrypted data is typically
Available online , doi: 10.11999/JEIT231169
Abstract:
In order to solve the problems of short-distance communication, lower system throughput and the effects of channel uncertainties in traditional Backscatter Communication (BackCom) systems, a robust resource allocation algorithm for a Reconfigurable Intelligent Surface (RIS)-assisted backscatter communication system with statistical Channel State Information (CSI) is proposed in this paper. A system weighting and sum throughput-maximization robust resource allocation model is formulated by considering the maximum transmit power constraint of the power station, the energy outage constraint and throughput outage constraint of backscatter nodes, the reflection coefficient constraint, the phase shift constraint of the RIS and the information transmission time constraint; Then, the original non-convex problem is transformed into a convex optimization problem by using the methods of Bernstein-type inequality, the alternating optimization, and the semi-definite relaxation technique. An iteration-based robust throughput maximization algorithm is designed. Simulation results show that the proposed algorithm had stronger robustness and higher throughput compared it with the traditional non-robust resource allocation algorithm and the resource allocation algorithm without RIS.
In order to solve the problems of short-distance communication, lower system throughput and the effects of channel uncertainties in traditional Backscatter Communication (BackCom) systems, a robust resource allocation algorithm for a Reconfigurable Intelligent Surface (RIS)-assisted backscatter communication system with statistical Channel State Information (CSI) is proposed in this paper. A system weighting and sum throughput-maximization robust resource allocation model is formulated by considering the maximum transmit power constraint of the power station, the energy outage constraint and throughput outage constraint of backscatter nodes, the reflection coefficient constraint, the phase shift constraint of the RIS and the information transmission time constraint; Then, the original non-convex problem is transformed into a convex optimization problem by using the methods of Bernstein-type inequality, the alternating optimization, and the semi-definite relaxation technique. An iteration-based robust throughput maximization algorithm is designed. Simulation results show that the proposed algorithm had stronger robustness and higher throughput compared it with the traditional non-robust resource allocation algorithm and the resource allocation algorithm without RIS.
Available online , doi: 10.11999/JEIT231118
Abstract:
In 5G Vehicular to Everything (5G-V2X), service messages are provided to a group of vehicles belonging to a specific region by means of point-to-multipoint transmission. To address security threats and privacy leakage, an authentication and key negotiation scheme is proposed for multicast service message transmission between content providers and vehicles. A certificate-less aggregated signature technique is used to batch-verify all vehicles in the group, and improves the efficiency of authentication requests. Secure key negotiation is realized based on the polynomial key management technique, which makes it impossible for illegal users or the core network to obtain the shared session key. Finally, a dynamic key update mechanism for vehicles in the group is implemented, so that when a vehicle joins or leaves the group, the content provider only needs to send a key update message to update the session key. The proposed scheme can guarantee security requirements such as anonymity, unlinkability, forward and backward security, and resistance to conspiracy attacks, as shown by formal verification tools and further security analysis. The computational efficiency is improved by about 34.2% compared to existing schemes.
In 5G Vehicular to Everything (5G-V2X), service messages are provided to a group of vehicles belonging to a specific region by means of point-to-multipoint transmission. To address security threats and privacy leakage, an authentication and key negotiation scheme is proposed for multicast service message transmission between content providers and vehicles. A certificate-less aggregated signature technique is used to batch-verify all vehicles in the group, and improves the efficiency of authentication requests. Secure key negotiation is realized based on the polynomial key management technique, which makes it impossible for illegal users or the core network to obtain the shared session key. Finally, a dynamic key update mechanism for vehicles in the group is implemented, so that when a vehicle joins or leaves the group, the content provider only needs to send a key update message to update the session key. The proposed scheme can guarantee security requirements such as anonymity, unlinkability, forward and backward security, and resistance to conspiracy attacks, as shown by formal verification tools and further security analysis. The computational efficiency is improved by about 34.2% compared to existing schemes.
Available online , doi: 10.11999/JEIT231222
Abstract:
Time Series Classification(TSC) is one of the most important and challenging tasks in the field of data mining. Deep learning techniques have achieved revolutionary progress in natural language processing and computer vision, and have also demonstrated great potential in areas such as time series analysis. A detailed review of the latest research advances in deep learning-based TSC is provided in this paper. Firstly, key terms and related concepts are defined. Secondly, the latest time series classification models are classified from four perspectives of network architectures: multilayer perceptron, convolutional neural networks, recurrent neural networks, and attention mechanisms, along with their respective advantages and limitations. Additionally, the latest developments and challenges in time series classification in the fields of human activity recognition and electroencephalogram-based emotion recognition are outlined. Finally, the unresolved issues and future research directions when applying deep learning to time series data are discussed. This paper provides researchers with a reference for understanding the latest research dynamics, new technologies, and development trends in the deep learning-based time series classification field.
Time Series Classification(TSC) is one of the most important and challenging tasks in the field of data mining. Deep learning techniques have achieved revolutionary progress in natural language processing and computer vision, and have also demonstrated great potential in areas such as time series analysis. A detailed review of the latest research advances in deep learning-based TSC is provided in this paper. Firstly, key terms and related concepts are defined. Secondly, the latest time series classification models are classified from four perspectives of network architectures: multilayer perceptron, convolutional neural networks, recurrent neural networks, and attention mechanisms, along with their respective advantages and limitations. Additionally, the latest developments and challenges in time series classification in the fields of human activity recognition and electroencephalogram-based emotion recognition are outlined. Finally, the unresolved issues and future research directions when applying deep learning to time series data are discussed. This paper provides researchers with a reference for understanding the latest research dynamics, new technologies, and development trends in the deep learning-based time series classification field.
Available online , doi: 10.11999/JEIT230984
Abstract:
In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins (DTs) caused by unreliable and delayed transmission in Radio Access Networks (RAN), a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed. The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs' sensing information in dual time scales, with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs' synchronization. Firstly, at large time scales, network slicing is employed to provide isolation for DTs with varying Quality of Service (QoS) and resolve deployment challenges; At small time scales, a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs' sensory information synchronization to dynamic environments. Secondly, in order to optimize the synchronization of DTs' sensory information at different time scales, a two-layer Deep Reinforcement Learning (DRL) framework is introduced to facilitate efficient network resource interaction, and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay (PER) mechanism to accelerate convergence speed. Finally, the effectiveness of the proposed strategy is validated through simulation results.
In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins (DTs) caused by unreliable and delayed transmission in Radio Access Networks (RAN), a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed. The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs' sensing information in dual time scales, with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs' synchronization. Firstly, at large time scales, network slicing is employed to provide isolation for DTs with varying Quality of Service (QoS) and resolve deployment challenges; At small time scales, a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs' sensory information synchronization to dynamic environments. Secondly, in order to optimize the synchronization of DTs' sensory information at different time scales, a two-layer Deep Reinforcement Learning (DRL) framework is introduced to facilitate efficient network resource interaction, and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay (PER) mechanism to accelerate convergence speed. Finally, the effectiveness of the proposed strategy is validated through simulation results.
Available online , doi: 10.11999/JEIT230923
Abstract:
Federated learning is an emerging distributed machine learning framework that effectively solves the problems of data silos and privacy leakage in traditional machine learning by performing joint modeling training without leaving the user’s private data out of the domain. However, federated learning suffers from the problem of training-lagged clients dragging down the global training speed. Related research has proposed asynchronous federated learning, which allows the users to upload to the server and participate in the aggregation task as soon as they finish updating their models locally, without waiting for the other users. However, asynchronous federated learning also suffers from the inability to recognize malicious models uploaded by malicious users and the problem of leaking user’s privacy. To address these issues, a privacy-preserving Secure Aggregation scheme for asynchronous Federated Learning(SAFL) is designed. The users add perturbations to locally trained models and upload the perturbed models to the server. The server detects and rejects the malicious users through a poisoning detection algorithm to achieve Secure Aggregation(SA). Finally, theoretical analysis and experiments show that in the scenario of asynchronous federated learning, the proposed scheme can effectively detect malicious users while protecting the privacy of users’ local models and reducing the risk of privacy leakage. The proposed scheme has also a significant improvement in the accuracy of the model compared with other schemes.
Federated learning is an emerging distributed machine learning framework that effectively solves the problems of data silos and privacy leakage in traditional machine learning by performing joint modeling training without leaving the user’s private data out of the domain. However, federated learning suffers from the problem of training-lagged clients dragging down the global training speed. Related research has proposed asynchronous federated learning, which allows the users to upload to the server and participate in the aggregation task as soon as they finish updating their models locally, without waiting for the other users. However, asynchronous federated learning also suffers from the inability to recognize malicious models uploaded by malicious users and the problem of leaking user’s privacy. To address these issues, a privacy-preserving Secure Aggregation scheme for asynchronous Federated Learning(SAFL) is designed. The users add perturbations to locally trained models and upload the perturbed models to the server. The server detects and rejects the malicious users through a poisoning detection algorithm to achieve Secure Aggregation(SA). Finally, theoretical analysis and experiments show that in the scenario of asynchronous federated learning, the proposed scheme can effectively detect malicious users while protecting the privacy of users’ local models and reducing the risk of privacy leakage. The proposed scheme has also a significant improvement in the accuracy of the model compared with other schemes.
Available online , doi: 10.11999/JEIT230992
Abstract:
To overcome the drawback of poor adaptability of existing unbiased self-synchronous scrambling code recognition algorithms at low Signal-to-Noise Ratios (SNR), a soft-judgement recognition method based on the log conditional likelihood ratio is proposed. Firstly, the linear constraint equations for the pairwise even-vector product of the self-synchronous scrambler of linear grouping codes and the self-synchronous scrambler of convolutional codes are constructed, and then the log likelihood ratio function is introduced, the log conditional likelihood ratio function based on the soft judgment is constructed, and the distribution characteristics of its mean and variance are analyzed. Finally the identification of self-synchronous scrambler of linear grouping codes and self-synchronous scrambler of convolutional codes is accomplished through binary assumption and the derived coresponding judgement threshold value. The simulations show that the proposed algorithm is able to complete the recognition of generating polynomials at low signal-to-noise ratios, and has a good low signal-to-noise adaptation capability. Compared with the recognition method based on solving the cost function, the recognition rate of the algorithm is greatly improved at signal-to-noise ratios below 3 dB, and when the recognition rate is 90%, the proposed algorithm achieves a performance gain of about 3 dB.
To overcome the drawback of poor adaptability of existing unbiased self-synchronous scrambling code recognition algorithms at low Signal-to-Noise Ratios (SNR), a soft-judgement recognition method based on the log conditional likelihood ratio is proposed. Firstly, the linear constraint equations for the pairwise even-vector product of the self-synchronous scrambler of linear grouping codes and the self-synchronous scrambler of convolutional codes are constructed, and then the log likelihood ratio function is introduced, the log conditional likelihood ratio function based on the soft judgment is constructed, and the distribution characteristics of its mean and variance are analyzed. Finally the identification of self-synchronous scrambler of linear grouping codes and self-synchronous scrambler of convolutional codes is accomplished through binary assumption and the derived coresponding judgement threshold value. The simulations show that the proposed algorithm is able to complete the recognition of generating polynomials at low signal-to-noise ratios, and has a good low signal-to-noise adaptation capability. Compared with the recognition method based on solving the cost function, the recognition rate of the algorithm is greatly improved at signal-to-noise ratios below 3 dB, and when the recognition rate is 90%, the proposed algorithm achieves a performance gain of about 3 dB.
Available online , doi: 10.11999/JEIT231192
Abstract:
The Aerial-Ground Integrated Networks (AGIN) take full advantage of the flexible deployment of Aerial Base Stations (ABSs) to provide on-demand coverage and high-quality services in hotspot areas. However, the high dynamics of ABSs pose a great challenge to service continuity assurance in AGIN. Furthermore, given the energy constraints of ABSs, ensuring service continuity with low power consumption becomes an increasingly formidable challenge. This is attributed to the inherent contradiction between enhancing service continuity and reducing power consumption, which typically necessitates distinct flight actions. Focusing on the problem mentionde above, a communication and control joint optimization approach based on Federated Deep Reinforcement Learning (FDRL) is proposed to obtain low-power service continuity assurance in AGIN. The proposed approach ensures service continuity by jointly optimizing the flight actions of ABSs, user associations, and power allocation. To cope with the high dynamics of ABSs, an environmental state experience pool is designed to capture the spatiotemporal correlation of channels, and the rate variance is introduced into the reward function to ensure service continuity. Taking into account the power consumption differences associated with various flight actions, the proposed approach optimizes the flight actions of ABSs to reduce their power consumption. Simulation results demonstrate that, under the premise of satisfying requirements for user rate and rate variance, the proposed approach can effectively reduce network power consumption. Additionally, the performance of FDRL is close to that of centralized reinforcement learning.
The Aerial-Ground Integrated Networks (AGIN) take full advantage of the flexible deployment of Aerial Base Stations (ABSs) to provide on-demand coverage and high-quality services in hotspot areas. However, the high dynamics of ABSs pose a great challenge to service continuity assurance in AGIN. Furthermore, given the energy constraints of ABSs, ensuring service continuity with low power consumption becomes an increasingly formidable challenge. This is attributed to the inherent contradiction between enhancing service continuity and reducing power consumption, which typically necessitates distinct flight actions. Focusing on the problem mentionde above, a communication and control joint optimization approach based on Federated Deep Reinforcement Learning (FDRL) is proposed to obtain low-power service continuity assurance in AGIN. The proposed approach ensures service continuity by jointly optimizing the flight actions of ABSs, user associations, and power allocation. To cope with the high dynamics of ABSs, an environmental state experience pool is designed to capture the spatiotemporal correlation of channels, and the rate variance is introduced into the reward function to ensure service continuity. Taking into account the power consumption differences associated with various flight actions, the proposed approach optimizes the flight actions of ABSs to reduce their power consumption. Simulation results demonstrate that, under the premise of satisfying requirements for user rate and rate variance, the proposed approach can effectively reduce network power consumption. Additionally, the performance of FDRL is close to that of centralized reinforcement learning.
Available online , doi: 10.11999/JEIT230917
Abstract:
The Unmanned Aerial Vehicle (UAV)assisted over-the-Air Computation(AirComp) system provides an effective solution for the fast aggregation of large-scale and distributed data. In this paper, a joint trajectory planning and power optimization method through UAV-assisted AirComp system is investigated. As a mobile base station, UAV is used to optimize the mean square error of the aggregated data of the AirComp system by adjusting its trajectory and transmitting power of the ground sensors. Under the limitations of UAV trajectory and sensor power, the UAV flight trajectory, the scaling factor and sensor power are jointly optmized to minimize the time-averaged mean square error. Based on the block coordinate descent and successive convex approximation methods, the joint optimization algorithm of UAV flight trajectory and power is proposed. Simulation results verify the performance of the proposed algorithm.
The Unmanned Aerial Vehicle (UAV)assisted over-the-Air Computation(AirComp) system provides an effective solution for the fast aggregation of large-scale and distributed data. In this paper, a joint trajectory planning and power optimization method through UAV-assisted AirComp system is investigated. As a mobile base station, UAV is used to optimize the mean square error of the aggregated data of the AirComp system by adjusting its trajectory and transmitting power of the ground sensors. Under the limitations of UAV trajectory and sensor power, the UAV flight trajectory, the scaling factor and sensor power are jointly optmized to minimize the time-averaged mean square error. Based on the block coordinate descent and successive convex approximation methods, the joint optimization algorithm of UAV flight trajectory and power is proposed. Simulation results verify the performance of the proposed algorithm.
Available online , doi: 10.11999/JEIT231047
Abstract:
To address the challenges of poor recognition effect of the infrared substation equipment image caused by multi-target, small target and occlusion target in complex background, an infrared image recognition method for substation equipment based on CenterNet is proposed. By combining the Adaptive Spatial Feature Fusion(ASFF) module and Feature Pyramid Networks (FPN), a feature fusion network with the structure of ASFF+FPN is constructed, and the cross-scale feature fusion capability of the model for multi-target and small target is enhanced, which excludes background information. To improve the feature capturing ability of occluding targets, the global attention mechanism is introduced to the feature fusion network to enhance target saliency. Additionally, depthwise separable convolution is introduced to reduce parameters number and model inference time, and a lightweight model is achieved. Finally, the problem of poor sensitivity to obscured targets is overcame by using the distribution focal loss function, and the convergence speed and recognition accuracy is improved. Tests are performed on a self-built dataset containing seven infrared substation equipment images. Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 95.19%, an improvement of 3.55% compared with the original algorithm, while it only has 32.52M model parameters. Furthermore, the method shows significant advantages in recognition accuracy and algorithm complexity, over four main target recognition algorithms.
To address the challenges of poor recognition effect of the infrared substation equipment image caused by multi-target, small target and occlusion target in complex background, an infrared image recognition method for substation equipment based on CenterNet is proposed. By combining the Adaptive Spatial Feature Fusion(ASFF) module and Feature Pyramid Networks (FPN), a feature fusion network with the structure of ASFF+FPN is constructed, and the cross-scale feature fusion capability of the model for multi-target and small target is enhanced, which excludes background information. To improve the feature capturing ability of occluding targets, the global attention mechanism is introduced to the feature fusion network to enhance target saliency. Additionally, depthwise separable convolution is introduced to reduce parameters number and model inference time, and a lightweight model is achieved. Finally, the problem of poor sensitivity to obscured targets is overcame by using the distribution focal loss function, and the convergence speed and recognition accuracy is improved. Tests are performed on a self-built dataset containing seven infrared substation equipment images. Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 95.19%, an improvement of 3.55% compared with the original algorithm, while it only has 32.52M model parameters. Furthermore, the method shows significant advantages in recognition accuracy and algorithm complexity, over four main target recognition algorithms.
Available online , doi: 10.11999/JEIT230949
Abstract:
In order to solve the problems of time synchronization accuracy degradation of two-way time transfer due to relative motion between nodes in Flying Ad hoc NETwork (FANET), a full-duplex Two-Way Time Transfer (TWTT) method is proposed. Firstly, a system of equations to be solved is constructed according to the full-duplex two-way time transfer procedure, and the synchronization error expression for single full-duplex two-way time transfer is derived. Then, the convergence of iteratively performing full-duplex two-way time transfer with or without frequency offset is analyzed. Finally, the performance of full-duplex two-way time transfer method is compared with traditional two-way time transfer methods by simulation analysis and experimental validation. The simulation and experimental results show that full-duplex two-way time transfer method can achieve the same time synchronization accuracy as the physical layer timestamps under high-speed maneuvering between nodes, and the synchronization accuracy is better than the existing motion compensation methods.
In order to solve the problems of time synchronization accuracy degradation of two-way time transfer due to relative motion between nodes in Flying Ad hoc NETwork (FANET), a full-duplex Two-Way Time Transfer (TWTT) method is proposed. Firstly, a system of equations to be solved is constructed according to the full-duplex two-way time transfer procedure, and the synchronization error expression for single full-duplex two-way time transfer is derived. Then, the convergence of iteratively performing full-duplex two-way time transfer with or without frequency offset is analyzed. Finally, the performance of full-duplex two-way time transfer method is compared with traditional two-way time transfer methods by simulation analysis and experimental validation. The simulation and experimental results show that full-duplex two-way time transfer method can achieve the same time synchronization accuracy as the physical layer timestamps under high-speed maneuvering between nodes, and the synchronization accuracy is better than the existing motion compensation methods.
Available online , doi: 10.11999/JEIT230803
Abstract:
Due to the interference and background drift between new and old task parameters, semantic segmentation model based on deep neural networks promotes catastrophic forgetting of old knowledge. Furthermore, information frequently cannot be stored owing to privacy concerns, security concerns, and other issues, which leads to model failure. Therefore, a continual semantic segmentation method based on gating mechanism and replay strategy is proposed. First, without storing old data, generative adversarial network and webpage crawling are used as data sources, the label evaluation module is used to solve the unsupervised problem and the background self-drawing module is used to solve background drift problem. Then, catastrophic forgetting is mitigateed by replay strategy; Finally, gated variables are used as a regularization means to increase the sparsity of the module and study the special case of gated variables combined with continual learning replay strategy. Our evaluation results on the Pascal VOC2012 dataset show that in the settings of complex scenario 10-2, Generative Adversarial Networks (GAN) and Web, the performance of the old task after all incremental steps are improved by 3.8% and 3.7% compared with the baseline, and in scenario 10-1, they are improved by 2.7% and 1.3% compared with the baseline, respectively.
Due to the interference and background drift between new and old task parameters, semantic segmentation model based on deep neural networks promotes catastrophic forgetting of old knowledge. Furthermore, information frequently cannot be stored owing to privacy concerns, security concerns, and other issues, which leads to model failure. Therefore, a continual semantic segmentation method based on gating mechanism and replay strategy is proposed. First, without storing old data, generative adversarial network and webpage crawling are used as data sources, the label evaluation module is used to solve the unsupervised problem and the background self-drawing module is used to solve background drift problem. Then, catastrophic forgetting is mitigateed by replay strategy; Finally, gated variables are used as a regularization means to increase the sparsity of the module and study the special case of gated variables combined with continual learning replay strategy. Our evaluation results on the Pascal VOC2012 dataset show that in the settings of complex scenario 10-2, Generative Adversarial Networks (GAN) and Web, the performance of the old task after all incremental steps are improved by 3.8% and 3.7% compared with the baseline, and in scenario 10-1, they are improved by 2.7% and 1.3% compared with the baseline, respectively.
Available online , doi: 10.11999/JEIT230627
Abstract:
To address the issue of dramatic fluctuations in user experience in legacy cellular networks, cell-free and legacy coexistence networks deploy Access Points (APs) into cellular networks, which can significantly improve the coverage signal quality of edge users and blind areas. Therefore, a good and consistent user experience at any location in the coverage area, i.e. consistent-coverage is the primary goal to improve the performance of coexistence networks. As the AP deployment is the determinant of user transmission rate and coverage in coexistence networks, a consistent-coverage oriented AP deployment optimization problem is designed. Firstly, the expression of the downlink achievable rate of each user is derived based on the joint transmission model of coexistence network. Secondly, a ratio sum optimization problem is proposed to maximize the average throughput. Finally, the non-convex problem is transformed into a convex optimization problem by using the fractional programming and the introduction of auxiliary variables, where the AP deployment scheme is obtained by the iterative solution. Compared with the legacy cellular networks, the simulation results demonstrate that the proposed scheme can significantly increase average throughput of the edge and blind areas.
To address the issue of dramatic fluctuations in user experience in legacy cellular networks, cell-free and legacy coexistence networks deploy Access Points (APs) into cellular networks, which can significantly improve the coverage signal quality of edge users and blind areas. Therefore, a good and consistent user experience at any location in the coverage area, i.e. consistent-coverage is the primary goal to improve the performance of coexistence networks. As the AP deployment is the determinant of user transmission rate and coverage in coexistence networks, a consistent-coverage oriented AP deployment optimization problem is designed. Firstly, the expression of the downlink achievable rate of each user is derived based on the joint transmission model of coexistence network. Secondly, a ratio sum optimization problem is proposed to maximize the average throughput. Finally, the non-convex problem is transformed into a convex optimization problem by using the fractional programming and the introduction of auxiliary variables, where the AP deployment scheme is obtained by the iterative solution. Compared with the legacy cellular networks, the simulation results demonstrate that the proposed scheme can significantly increase average throughput of the edge and blind areas.
Available online , doi: 10.11999/JEIT230935
Abstract:
Considering the problem of blind identification of Unique Words (UW) for Time Division Multiple Access (TDMA) signals in non-cooperative communication, a blind identification algorithm for distributed UW is proposed in this paper. Different from the unique codes recognition algorithm at the bit layer, a unique words recognition algorithm at the waveform layer oriented to the correlation is proposed between different windows of the modulated data for centralised unique words and distributed unique words, respectively. The algorithm takes advantage of the consistency and correlation of the unique words and proceeds in two steps: firstly, the unique words of different burst signals are vertically aligned by eliminating the effects of frequency and phase bias between the different burst signals through differential accumulation, and then the positions and lengths of the unique words are identified by the multilayer differential conjugate fourth order correlation algorithm. The performance of the algorithm is simulated and analysed with different number of bursts, signal-to-noise ratios, and with or without frequency and phase biases, and the effectiveness of the waveform layer identification of unique words is verified, and the algorithm achieves more than 95% of the identification rate at a signal-to-noise ratio of 5dB for both centralized and distributed unique words, which is of certain value for engineering applications.
Considering the problem of blind identification of Unique Words (UW) for Time Division Multiple Access (TDMA) signals in non-cooperative communication, a blind identification algorithm for distributed UW is proposed in this paper. Different from the unique codes recognition algorithm at the bit layer, a unique words recognition algorithm at the waveform layer oriented to the correlation is proposed between different windows of the modulated data for centralised unique words and distributed unique words, respectively. The algorithm takes advantage of the consistency and correlation of the unique words and proceeds in two steps: firstly, the unique words of different burst signals are vertically aligned by eliminating the effects of frequency and phase bias between the different burst signals through differential accumulation, and then the positions and lengths of the unique words are identified by the multilayer differential conjugate fourth order correlation algorithm. The performance of the algorithm is simulated and analysed with different number of bursts, signal-to-noise ratios, and with or without frequency and phase biases, and the effectiveness of the waveform layer identification of unique words is verified, and the algorithm achieves more than 95% of the identification rate at a signal-to-noise ratio of 5dB for both centralized and distributed unique words, which is of certain value for engineering applications.
Available online , doi: 10.11999/JEIT231012
Abstract:
Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.
Most of the existing intelligent algorithms for identifying real and false targets are based on supervised learning and perform poorly under a low signal-to-noise ratio. Considering the above problems, an unsupervised clustering identification method of real and false targets based on frequency response features in multi-Coherent Processing Intervals(CPIs) is proposed by using the variability and uniqueness of the scattering characteristics of real and false targets in multi-CPIs, respectively. Firstly, the real and false targets are windowed and truncated along the fast time dimension in the fast-slow time domain, and the fast-slow time domain frequency response features are extracted to construct a preliminary sample set. Then, the real and false targets are identified by a two-step recognition algorithm composed of an Agglomerative clustering and a feature fusion network. Finally, a multi-CPI joint decision method is proposed to improve the recognition performance and reliability. It is proved by simulation and measured data that the proposed method can effectively identify real targets and multiple active false targets.
Available online , doi: 10.11999/JEIT230721
Abstract:
For the analysis problem of large antenna arrays with radomes, based on the equivalence principle and mode matching theory, the wave port model of Multilevel Fast Multipole Algorithm (MLFMA) is established, and the accurate electromagnetic modeling of antenna excitation source and matching load is realized. Moreover, a parallel strategy of MLFMA for calculating metal-dielectric antenna models is proposed. By establishing multiple octree structures, the communication in processes during the calculation is reduced, and the accurate and efficient analysis of large antenna-array-and-radome-integration system is realized. A comparison of the antenna pattern and S parameters calculated by the proposed algorithm, the higher order method of moments and the finite element-boundary integral is given, validating accuracy and efficiency of the proposed method.
For the analysis problem of large antenna arrays with radomes, based on the equivalence principle and mode matching theory, the wave port model of Multilevel Fast Multipole Algorithm (MLFMA) is established, and the accurate electromagnetic modeling of antenna excitation source and matching load is realized. Moreover, a parallel strategy of MLFMA for calculating metal-dielectric antenna models is proposed. By establishing multiple octree structures, the communication in processes during the calculation is reduced, and the accurate and efficient analysis of large antenna-array-and-radome-integration system is realized. A comparison of the antenna pattern and S parameters calculated by the proposed algorithm, the higher order method of moments and the finite element-boundary integral is given, validating accuracy and efficiency of the proposed method.
Available online , doi: 10.11999/JEIT230815
Abstract:
In the research on utilizing emerging non-volatile storage arrays for in-memory computing, the latency of in-memory multipliers often exhibits exponential growth with increasing bit width, and significantly impacts the computational performance. A Voltage-Gated Spin-Orbit Torque Magnetoresistive Random-Acess Memory (VGSOT-MRAM) device unit crossbar array is proposed and a circuit design approach for in-memory Wallace tree multipliers is presented in this paper. The proposed series-connected storage unit structure effectively addresses the issue of low resistance values in magnetic storage units through resistive summing. Furthermore, an in-memory computing architecture based on a voltage-controlled spin-orbit torque magnetic storage unit crossbar array is introduced. Finally, a five-input majority decision logic gate implemented during the “read” operation is leveraged to further reduce the logic depth of the Wallace tree multiplier. Compared to existing multiplier design methods, the proposed approach reduces the delay overhead from O(n2) to O(log2 n), with even lower latency for larger bit widths.
In the research on utilizing emerging non-volatile storage arrays for in-memory computing, the latency of in-memory multipliers often exhibits exponential growth with increasing bit width, and significantly impacts the computational performance. A Voltage-Gated Spin-Orbit Torque Magnetoresistive Random-Acess Memory (VGSOT-MRAM) device unit crossbar array is proposed and a circuit design approach for in-memory Wallace tree multipliers is presented in this paper. The proposed series-connected storage unit structure effectively addresses the issue of low resistance values in magnetic storage units through resistive summing. Furthermore, an in-memory computing architecture based on a voltage-controlled spin-orbit torque magnetic storage unit crossbar array is introduced. Finally, a five-input majority decision logic gate implemented during the “read” operation is leveraged to further reduce the logic depth of the Wallace tree multiplier. Compared to existing multiplier design methods, the proposed approach reduces the delay overhead from O(n2) to O(log2 n), with even lower latency for larger bit widths.
Available online , doi: 10.11999/JEIT230874
Abstract:
Directed Acyclic Graph(DAG)-based blockchain can significantly improve system performance and have become a research topic in both academia and industry. Compared with the traditional chain-based blockchains with serialization, DAG-based blockchains can process multiple blocks concurrently to package significant transactions into the chain. With the surge in transaction throughput, DAG-based blockchain faces the issue of low transaction execution efficiency, i.e., the demand for state data access for massive transaction execution increases dramatically, resulting in high Input/Output(I/O) overhead. Enabling low I/O state access mainly encounters two new challenges. On the one hand, if DAG-based blockchain directly adopts the traditional state prefetch mechanism, it will introduce a large number of stale reads due to inconsistent execution logic. On the other hand, state access for different accounts causes duplicate I/O overhead in the upper nodes of the Merkle tree. To this end, an efficient transaction execution mechanism based on pipelining – ErlangShen is designed, including the epoch granularity state prefetch mechanism and Merkle high-level path buffer mechanism to reduce the number of stale reads and duplicate I/O overhead, respectively. Specifically, ErlangShen leverages the complicated logic and severe conflicts of transactions accessing hotspot states to parallelize the execution of hotspot transactions and the prefetch of states accessed by cold transactions, to avoid the implication of the state prefetching on the transaction execution. Furthermore, the customized concurrency control methods is designed according to the data access pattern of hotspot and cold states to further improve the system throughput. Experimental results show that ErlangShen can reduce the number of stale reads by about 90% and improve transaction processing performance by 3～4x compared to Nezha, the state-of-the-art DAG-based blockchain transaction processing solution.
Directed Acyclic Graph(DAG)-based blockchain can significantly improve system performance and have become a research topic in both academia and industry. Compared with the traditional chain-based blockchains with serialization, DAG-based blockchains can process multiple blocks concurrently to package significant transactions into the chain. With the surge in transaction throughput, DAG-based blockchain faces the issue of low transaction execution efficiency, i.e., the demand for state data access for massive transaction execution increases dramatically, resulting in high Input/Output(I/O) overhead. Enabling low I/O state access mainly encounters two new challenges. On the one hand, if DAG-based blockchain directly adopts the traditional state prefetch mechanism, it will introduce a large number of stale reads due to inconsistent execution logic. On the other hand, state access for different accounts causes duplicate I/O overhead in the upper nodes of the Merkle tree. To this end, an efficient transaction execution mechanism based on pipelining – ErlangShen is designed, including the epoch granularity state prefetch mechanism and Merkle high-level path buffer mechanism to reduce the number of stale reads and duplicate I/O overhead, respectively. Specifically, ErlangShen leverages the complicated logic and severe conflicts of transactions accessing hotspot states to parallelize the execution of hotspot transactions and the prefetch of states accessed by cold transactions, to avoid the implication of the state prefetching on the transaction execution. Furthermore, the customized concurrency control methods is designed according to the data access pattern of hotspot and cold states to further improve the system throughput. Experimental results show that ErlangShen can reduce the number of stale reads by about 90% and improve transaction processing performance by 3～4x compared to Nezha, the state-of-the-art DAG-based blockchain transaction processing solution.
Available online , doi: 10.11999/JEIT230699
Abstract:
Energy Efficiency (EE) is an important design metric for 5G+/6G wireless communications, and Reconfigurable Intelligent Surface (RIS) is widely recognized as a potential means to improve EE. Unlike passive RIS, hybrid RIS consists of both active and passive components, which can amplify the signal strength while phase-shifting the incoming wave, and can effectively overcome the “multiplicative fading” effect caused by fully passive RIS. In view of this, a hybrid RIS-assisted Integrated Sensing and Communication (ISAC) downlink transmission system is proposed in this paper. In order to investigate the intrinsic correlation between data transmission capacity and energy consumption, the paper jointly optimizes the beamforming and phase-shifting of hybrid RIS at the Base Station (BS) under the constraints of BS transmit power, beampattern gain, and hybrid RIS power and amplitude with the goal of maximizing the global EE in a multiuser network. To solve this complex fractional programming problem, an algorithm based on Alternating Optimization (AO) is proposed to solve it. To overcome the problem of high algorithm complexity caused by the introduction of auxiliary variables in the AO algorithm, a solution algorithm based on a cascaded deep learning network is proposed using the association of coupled optimization variables. Simulation results show that the proposed hybrid RIS-assisted ISAC scheme outperforms existing schemes in terms of sum rate and EE, and the algorithm converges quickly.
Energy Efficiency (EE) is an important design metric for 5G+/6G wireless communications, and Reconfigurable Intelligent Surface (RIS) is widely recognized as a potential means to improve EE. Unlike passive RIS, hybrid RIS consists of both active and passive components, which can amplify the signal strength while phase-shifting the incoming wave, and can effectively overcome the “multiplicative fading” effect caused by fully passive RIS. In view of this, a hybrid RIS-assisted Integrated Sensing and Communication (ISAC) downlink transmission system is proposed in this paper. In order to investigate the intrinsic correlation between data transmission capacity and energy consumption, the paper jointly optimizes the beamforming and phase-shifting of hybrid RIS at the Base Station (BS) under the constraints of BS transmit power, beampattern gain, and hybrid RIS power and amplitude with the goal of maximizing the global EE in a multiuser network. To solve this complex fractional programming problem, an algorithm based on Alternating Optimization (AO) is proposed to solve it. To overcome the problem of high algorithm complexity caused by the introduction of auxiliary variables in the AO algorithm, a solution algorithm based on a cascaded deep learning network is proposed using the association of coupled optimization variables. Simulation results show that the proposed hybrid RIS-assisted ISAC scheme outperforms existing schemes in terms of sum rate and EE, and the algorithm converges quickly.
Available online , doi: 10.11999/JEIT230817
Abstract:
A novel method for evaluating interference performance based on Transfer Learning(TL) and parameter optimization is proposed to address the limitation of single evaluation results obtained using traditional error rate assessment in digital communication systems. This method selects the core parameters of each signal processing module as the training index of machine learning and considers the evaluation results of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) as the classification standard. A Support Vector Machine (SVM) is used to train and evaluate the model. The parameter optimization problem in the SVM is addressed by enhancing the global search capability of Ant Colony Optimization (ACO). Moreover, the issue of missing data in the training samples is solved based on the knowledge transfer properties of TL. The results of the simulation experiments demonstrate that the SVM with access to the source domain dataset increases the model accuracy by 4.2%. Parameter optimization, which sacrifices the initial convergence ability, enhances the proximity to the optimal solution by 4.7%.In addition, it can be employed to evaluate the interference performance of digital communication systems.
A novel method for evaluating interference performance based on Transfer Learning(TL) and parameter optimization is proposed to address the limitation of single evaluation results obtained using traditional error rate assessment in digital communication systems. This method selects the core parameters of each signal processing module as the training index of machine learning and considers the evaluation results of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) as the classification standard. A Support Vector Machine (SVM) is used to train and evaluate the model. The parameter optimization problem in the SVM is addressed by enhancing the global search capability of Ant Colony Optimization (ACO). Moreover, the issue of missing data in the training samples is solved based on the knowledge transfer properties of TL. The results of the simulation experiments demonstrate that the SVM with access to the source domain dataset increases the model accuracy by 4.2%. Parameter optimization, which sacrifices the initial convergence ability, enhances the proximity to the optimal solution by 4.7%.In addition, it can be employed to evaluate the interference performance of digital communication systems.
Available online , doi: 10.11999/JEIT230713
Abstract:
Considering the problem that traditional Field Programmable Gate Array (FPGA)-based Convolutional Neural Network(CNN) accelerators in computer vision are not adapted to Vision Transformer networks, a unified FPGA accelerator for convolutional neural networks and Transformer is proposed. First, a generalized computation mapping method for FPGA is proposed based on the characteristics of convolution and attention mechanisms. Second, a nonlinear acceleration unit is proposed to provide acceleration support for multiple nonlinear operations in computer vision networks. Then, we implemented the accelerator design on Xilinx XCVU37P FPGA. Experimental results show that the proposed nonlinear acceleration unit improves the throughput while causing only a small accuracy loss. ResNet-50 and ViT-B/16 achieved 589.94 GOPS and 564.76 GOPS performance on the proposed FPGA accelerator. Compared to the GPU implementation, energy efficiency is improved by a factor of 5.19 and 7.17, respectively. Compared with other large FPGA-based designs, the energy efficiency is significantly improved, while the computing efficiency is increased by 8.02%～177.53% compared to other FPGA accelerators.
Considering the problem that traditional Field Programmable Gate Array (FPGA)-based Convolutional Neural Network(CNN) accelerators in computer vision are not adapted to Vision Transformer networks, a unified FPGA accelerator for convolutional neural networks and Transformer is proposed. First, a generalized computation mapping method for FPGA is proposed based on the characteristics of convolution and attention mechanisms. Second, a nonlinear acceleration unit is proposed to provide acceleration support for multiple nonlinear operations in computer vision networks. Then, we implemented the accelerator design on Xilinx XCVU37P FPGA. Experimental results show that the proposed nonlinear acceleration unit improves the throughput while causing only a small accuracy loss. ResNet-50 and ViT-B/16 achieved 589.94 GOPS and 564.76 GOPS performance on the proposed FPGA accelerator. Compared to the GPU implementation, energy efficiency is improved by a factor of 5.19 and 7.17, respectively. Compared with other large FPGA-based designs, the energy efficiency is significantly improved, while the computing efficiency is increased by 8.02%～177.53% compared to other FPGA accelerators.
Available online , doi: 10.11999/JEIT230625
Abstract:
Orthogonal Time Frequency Space (OTFS) can convert the doubly-selective channels into non-selective channels in the Delay-Doppler (DD) domain, which provides a solution for establishing reliable wireless communication in high-mobility scenarios. However, serious Inter-Doppler Interference (IDI) exists in complex multi-scattering scenarios such as internet of vehicles, which brings great challenges to the accurate demodulation of OTFS receiver signals. To solve these problems, a kind of joint Sparse Bayesian Learning (SBL) and damped Least Square Minimum Residual (d-LSMR) OTFS receiver is proposed. Firstly, based on the relationship between OTFS time domain and DD domain, the channel estimation problem is transformed into a Basis Expansion Model (BEM) to accurately estimate DD domain channels including Doppler sampling points. Then, an efficient conversion algorithm is proposed to convert the basis coefficients into channel equivalent matrix. Additionally, the noise estimated in channel estimation is used in d-LSMR equalizer, and the sparse channel matrix in DD domain is adopted to achieve fast convergence. System simulation results show that compared with the current representative OTFS receiver, the proposed scheme achieves better bit error rate performance and reduces the computational complexity.
Orthogonal Time Frequency Space (OTFS) can convert the doubly-selective channels into non-selective channels in the Delay-Doppler (DD) domain, which provides a solution for establishing reliable wireless communication in high-mobility scenarios. However, serious Inter-Doppler Interference (IDI) exists in complex multi-scattering scenarios such as internet of vehicles, which brings great challenges to the accurate demodulation of OTFS receiver signals. To solve these problems, a kind of joint Sparse Bayesian Learning (SBL) and damped Least Square Minimum Residual (d-LSMR) OTFS receiver is proposed. Firstly, based on the relationship between OTFS time domain and DD domain, the channel estimation problem is transformed into a Basis Expansion Model (BEM) to accurately estimate DD domain channels including Doppler sampling points. Then, an efficient conversion algorithm is proposed to convert the basis coefficients into channel equivalent matrix. Additionally, the noise estimated in channel estimation is used in d-LSMR equalizer, and the sparse channel matrix in DD domain is adopted to achieve fast convergence. System simulation results show that compared with the current representative OTFS receiver, the proposed scheme achieves better bit error rate performance and reduces the computational complexity.
Available online , doi: 10.11999/JEIT230921
Abstract:
Herein, the Shuangqing-1(Luojia3-01) multimode imaging sample dataset is presented to address the problem of limited data types provided for user services by remote sensing satellites with the highest resolution. This dataset includes various imaging modes, such as push-scan, array push-frame, and video staring; hence, it covers typical data samples from different target areas ,such as urban regions, water bodies, mountainous regions, and airports. The construction of this dataset involves signal data decoding, Bayer interpolation, relative radiometric correction, geometric positioning, video stabilization, and three-dimensional reconstruction. Additionally, in-depth discussions and investigations are conducted on key algorithms, such as on-orbit calibration, rapid production of area of interest products, high-definition video geometric stabilization, and multi-angle three-dimensional reconstruction. Finally, the sample dataset is visually displayed and quantitatively evaluated from three aspects: image standard, video staring, and real-world three-dimensional products.
Herein, the Shuangqing-1(Luojia3-01) multimode imaging sample dataset is presented to address the problem of limited data types provided for user services by remote sensing satellites with the highest resolution. This dataset includes various imaging modes, such as push-scan, array push-frame, and video staring; hence, it covers typical data samples from different target areas ,such as urban regions, water bodies, mountainous regions, and airports. The construction of this dataset involves signal data decoding, Bayer interpolation, relative radiometric correction, geometric positioning, video stabilization, and three-dimensional reconstruction. Additionally, in-depth discussions and investigations are conducted on key algorithms, such as on-orbit calibration, rapid production of area of interest products, high-definition video geometric stabilization, and multi-angle three-dimensional reconstruction. Finally, the sample dataset is visually displayed and quantitatively evaluated from three aspects: image standard, video staring, and real-world three-dimensional products.
Available online , doi: 10.11999/JEIT230212
Abstract:
Due to its unique advantages of strong survivability and seamless coverage, Satellite Communication (SatCom) can make up for the shortcomings of ground communication such as terrain limitations and small coverage, and has become increasingly important in current and future communication systems. In addition, aerial-assisted communication is considered a valuable research direction due to its flexibility and scalability in satellite ground networks. To overcome the problems of spectrum shortage and low spectrum utilization in Integrated Satellite-Aerial-Terrestrial Network (ISATN), Cognitive Radio (CR) and Non-Orthogonal Multiple Access (NOMA) are used in wireless communication networks to improve spectrum utilization and transmission performance. In this regard, the performance of a NOMA-based Cognitive Integrated Satellite-Aerial-Terrestrial Network(CISATN) with multiple primary users is studied, and accurate expressions for Outage Probability (OP) and ergodic capacity of the primary and secondary networks are derived. Asymptotic expressions for the OP and diversity order of these two networks are provided to obtain further insights. Finally, the correctness of the theoretical derivation is verified through numerical simulation, and the impact of key variables on system indicators is analyzed.
Due to its unique advantages of strong survivability and seamless coverage, Satellite Communication (SatCom) can make up for the shortcomings of ground communication such as terrain limitations and small coverage, and has become increasingly important in current and future communication systems. In addition, aerial-assisted communication is considered a valuable research direction due to its flexibility and scalability in satellite ground networks. To overcome the problems of spectrum shortage and low spectrum utilization in Integrated Satellite-Aerial-Terrestrial Network (ISATN), Cognitive Radio (CR) and Non-Orthogonal Multiple Access (NOMA) are used in wireless communication networks to improve spectrum utilization and transmission performance. In this regard, the performance of a NOMA-based Cognitive Integrated Satellite-Aerial-Terrestrial Network(CISATN) with multiple primary users is studied, and accurate expressions for Outage Probability (OP) and ergodic capacity of the primary and secondary networks are derived. Asymptotic expressions for the OP and diversity order of these two networks are provided to obtain further insights. Finally, the correctness of the theoretical derivation is verified through numerical simulation, and the impact of key variables on system indicators is analyzed.
Available online , doi: 10.11999/JEIT221203
Abstract:
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
Available online , doi: 10.11999/JEIT210265
Abstract:
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
Available online , doi: 10.11999/JEIT201066
Abstract:
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
