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2024 Vol. 46, No. 6
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2024, 46(6): 2299-2310.
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.
2024, 46(6): 2311-2322.
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.
2024, 46(6): 2323-2334.
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.
2024, 46(6): 2335-2351.
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 outlooked 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 outlooked for, which lays a foundation for future research work.
2024, 46(6): 2352-2360.
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.
2024, 46(6): 2361-2371.
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 not only improves 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 not only improves 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.
2024, 46(6): 2372-2381.
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 coefficients and cooperative node selection variables, 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 coefficients and cooperative node selection variables, 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.
2024, 46(6): 2382-2390.
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 shown by simulation results that 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 shown by simulation results that 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.
2024, 46(6): 2391-2400.
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 addresses 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 the 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 addresses 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 the 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.
2024, 46(6): 2401-2408.
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.
2024, 46(6): 2409-2417.
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.
2024, 46(6): 2418-2424.
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.
2024, 46(6): 2425-2433.
doi: 10.11999/JEIT230706
Abstract:
To achieve an accurate characterization and thorough understanding of the millimeter-Wave (mmWave) communication channel propagation mechanism, precise characterization of diffuse scattering propagation and polarization is crucial. These aspects are also indispensable for the development of a high-precision mmWave channel model. In response to the insufficient characterization of diffuse scattering propagation and polarization caused by building materials in the mmWave frequency bands, a novel diffuse scattering depolarization modeling method based on effective roughness theory is presented in this paper. Initially, the electric field of the diffuse scattering radiation produced by the rough surface of building materials is decomposed along the polarization dimension of the electromagnetic wave. Subsequently, the depolarization coefficient is introduced to establish the propagation model. The paper investigates the diffuse scattering propagation and polarization characteristics, encompassing the power angle spectrum, depolarization coefficient, and cross-polarization discrimination ratio, by utilizing measured data of typical materials in the 40～50 GHz frequency range. The obtained results validate that the proposed model effectively captures the polarization characteristics of building materials with both rough and smooth surfaces. The achieved depolarization conversion rates are 39% and 4%, respectively.
To achieve an accurate characterization and thorough understanding of the millimeter-Wave (mmWave) communication channel propagation mechanism, precise characterization of diffuse scattering propagation and polarization is crucial. These aspects are also indispensable for the development of a high-precision mmWave channel model. In response to the insufficient characterization of diffuse scattering propagation and polarization caused by building materials in the mmWave frequency bands, a novel diffuse scattering depolarization modeling method based on effective roughness theory is presented in this paper. Initially, the electric field of the diffuse scattering radiation produced by the rough surface of building materials is decomposed along the polarization dimension of the electromagnetic wave. Subsequently, the depolarization coefficient is introduced to establish the propagation model. The paper investigates the diffuse scattering propagation and polarization characteristics, encompassing the power angle spectrum, depolarization coefficient, and cross-polarization discrimination ratio, by utilizing measured data of typical materials in the 40～50 GHz frequency range. The obtained results validate that the proposed model effectively captures the polarization characteristics of building materials with both rough and smooth surfaces. The achieved depolarization conversion rates are 39% and 4%, respectively.
2024, 46(6): 2434-2442.
doi: 10.11999/JEIT231395
Abstract:
To meet the escalating demands for efficient communication and reliable sensing, a Reconfigurable Intelligent Surface(RIS)-assisted Simultaneous Wireless Information and Power Transfer(SWIPT)-Non-Orthogonal Multiple Access(NOMA) system is proposed in this paper. This system is designed to concurrently achieve target sensing and information transmission. Considering the imperfections of Successive Interference Cancellation (SIC) and Channel Estimation Error (CEE), a comprehensive analysis of the system’s reliability, effectiveness, and radar sensing performance is conducted. Analytical expressions for the Outage Probability (OP), Ergodic Rate (ER), Probability of Detection (PoD), and Radar Estimation Information Rate (REIR) of the system are derived to provide insights into its performance. The analysis results reveal the following findings: the presence of imperfect SIC and CEE adversely impacts the system’s performance; the OP diminishes as the transmitted power of base station increases, eventually converging to a constant value in the high Signal-to-Noise Ratio (SNR) region; both the ER and the REIR increase with the base station’s transmitted power and eventually stabilize at an upper limit value in the high SNR region; the PoD increases with the base station’s transmit power at different detection thresholds; the Joint Radar Detection and Communication Coverage Probability (JRDCCP) decreases with the outage threshold and detection threshold, respectively.
To meet the escalating demands for efficient communication and reliable sensing, a Reconfigurable Intelligent Surface(RIS)-assisted Simultaneous Wireless Information and Power Transfer(SWIPT)-Non-Orthogonal Multiple Access(NOMA) system is proposed in this paper. This system is designed to concurrently achieve target sensing and information transmission. Considering the imperfections of Successive Interference Cancellation (SIC) and Channel Estimation Error (CEE), a comprehensive analysis of the system’s reliability, effectiveness, and radar sensing performance is conducted. Analytical expressions for the Outage Probability (OP), Ergodic Rate (ER), Probability of Detection (PoD), and Radar Estimation Information Rate (REIR) of the system are derived to provide insights into its performance. The analysis results reveal the following findings: the presence of imperfect SIC and CEE adversely impacts the system’s performance; the OP diminishes as the transmitted power of base station increases, eventually converging to a constant value in the high Signal-to-Noise Ratio (SNR) region; both the ER and the REIR increase with the base station’s transmitted power and eventually stabilize at an upper limit value in the high SNR region; the PoD increases with the base station’s transmit power at different detection thresholds; the Joint Radar Detection and Communication Coverage Probability (JRDCCP) decreases with the outage threshold and detection threshold, respectively.
2024, 46(6): 2443-2451.
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.
2024, 46(6): 2452-2461.
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.
2024, 46(6): 2462-2469.
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.
2024, 46(6): 2470-2479.
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 (TOPSIS) 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 (TOPSIS) 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.
2024, 46(6): 2480-2487.
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.
2024, 46(6): 2488-2496.
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 as 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 an 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 as 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 an 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.
2024, 46(6): 2497-2505.
doi: 10.11999/JEIT231388
Abstract:
Access latency and complexity in the satellite Internet of Things (IoT) are significantly reduced by the grant-free uplink random access based on Slotted ALOHA (S-ALOHA). However, with the increase of the number of IoT users, the collision probability of S-ALOHA is markedly increased, thereby impacting the performance of the system. This paper addresses the scenario of massive device uplink access in satellite IoT, focusing on the investigation of power resource control for IoT terminals to achieve maximization of system rate. A flexible multiple access scheme based on S-ALOHA is proposed. In the presence of collisions in the system, transmission is carried out using non-orthogonal multiple access technology, which mitigates the issue of repeated transmission of user information and reduces transmission latency. The sequential decision problem of maximizing system rate under the constraint of terminal power is modeled as a Markov process, and the Advantage Actor-Critic (A2C) method is employed to solve it. The simulation results indicate that the success rate of terminal access in scenarios with a massive number of IoT terminals is effectively ensured by the proposed flexible multiple access technology. Additionally, the resource allocation algorithm based on A2C is shown to outperform traditional resource allocation algorithms.
Access latency and complexity in the satellite Internet of Things (IoT) are significantly reduced by the grant-free uplink random access based on Slotted ALOHA (S-ALOHA). However, with the increase of the number of IoT users, the collision probability of S-ALOHA is markedly increased, thereby impacting the performance of the system. This paper addresses the scenario of massive device uplink access in satellite IoT, focusing on the investigation of power resource control for IoT terminals to achieve maximization of system rate. A flexible multiple access scheme based on S-ALOHA is proposed. In the presence of collisions in the system, transmission is carried out using non-orthogonal multiple access technology, which mitigates the issue of repeated transmission of user information and reduces transmission latency. The sequential decision problem of maximizing system rate under the constraint of terminal power is modeled as a Markov process, and the Advantage Actor-Critic (A2C) method is employed to solve it. The simulation results indicate that the success rate of terminal access in scenarios with a massive number of IoT terminals is effectively ensured by the proposed flexible multiple access technology. Additionally, the resource allocation algorithm based on A2C is shown to outperform traditional resource allocation algorithms.
2024, 46(6): 2506-2514.
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.
2024, 46(6): 2515-2524.
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. An SVM (Support Vector Machine) 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. An SVM (Support Vector Machine) 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.
2024, 46(6): 2525-2532.
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.
2024, 46(6): 2533-2541.
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.
2024, 46(6): 2542-2548.
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.
2024, 46(6): 2549-2557.
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.
Research on Symmetrically Resonant VLF Transmit/Receive Magnetoelectric Antenna Coupling Performance
2024, 46(6): 2558-2567.
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. Experimental results indicate that obtained at resonant frequencies, the ME transmit/receive antenna is improved by 82.6% in output voltage and by 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. Experimental results indicate that obtained at resonant frequencies, the ME transmit/receive antenna is improved by 82.6% in output voltage and by 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.
2024, 46(6): 2568-2577.
doi: 10.11999/JEIT230717
Abstract:
DNA molecules have the advantages of high density and stability, and are expected to become the medium for the next generation of massive data storage needs, which has received widespread attention in recent years. Currently, primers are used as the unique identifier for files, and random retrieval of DNA pool storage files can be achieved based on polymerase chain reaction (PCR) amplification technology. However, the allocation and mapping relationship between primers and files have not been thoroughly studied, and random allocation is still used to associate primers and files. This will lead to a decrease in the search efficiency of the target primer sequence, and saving the mapping relationship table between primers and files will cause a lot of data redundancy. In order to provide an efficient connection bridge between silicon-based computing devices and carbon-based storage systems, and effectively reduce the data redundancy caused by storing primer-file mapping relationships, a random retrieval method for DNA storage based on the primer index matrix is proposed in this paper. This method constructs a primer index matrix by dividing the stored file set according to different attributes of the file, and converts the primers in the primer library into an ordered primer library according to conversion rules. Finally, the mapping relationship between primers and files is optimized to achieve efficient and multi-dimensional retrieval during file random retrieval. The experimental results show that when storing file sets of different sizes, the efficiency of primer retrieval is improved to a constant level of time complexity by establishing the corresponding primer index matrix using the proposed algorithm in this paper, and the extra storage space required to store the mapping relationship between primers and files is optimized from linear growth to logarithmic growth.
DNA molecules have the advantages of high density and stability, and are expected to become the medium for the next generation of massive data storage needs, which has received widespread attention in recent years. Currently, primers are used as the unique identifier for files, and random retrieval of DNA pool storage files can be achieved based on polymerase chain reaction (PCR) amplification technology. However, the allocation and mapping relationship between primers and files have not been thoroughly studied, and random allocation is still used to associate primers and files. This will lead to a decrease in the search efficiency of the target primer sequence, and saving the mapping relationship table between primers and files will cause a lot of data redundancy. In order to provide an efficient connection bridge between silicon-based computing devices and carbon-based storage systems, and effectively reduce the data redundancy caused by storing primer-file mapping relationships, a random retrieval method for DNA storage based on the primer index matrix is proposed in this paper. This method constructs a primer index matrix by dividing the stored file set according to different attributes of the file, and converts the primers in the primer library into an ordered primer library according to conversion rules. Finally, the mapping relationship between primers and files is optimized to achieve efficient and multi-dimensional retrieval during file random retrieval. The experimental results show that when storing file sets of different sizes, the efficiency of primer retrieval is improved to a constant level of time complexity by establishing the corresponding primer index matrix using the proposed algorithm in this paper, and the extra storage space required to store the mapping relationship between primers and files is optimized from linear growth to logarithmic growth.
2024, 46(6): 2578-2586.
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.
2024, 46(6): 2587-2595.
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.
2024, 46(6): 2596-2604.
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. The 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. The 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.
2024, 46(6): 2605-2618.
doi: 10.11999/JEIT231108
Abstract:
Deep spatiotemporal sequence prediction methods that incorporate a priori physical knowledge are commonly characterized by the utilization of Partial Differential Equations (PDE) for modeling. However, two main issues are concerned: (1) the limited precision in approximations with PDEs; and (2) the inability to efficiently capture spatiotemporal features at multiple spatial scales as well as the edge spatial information of the spatiotemporal sequences in the recurrent network. To address these challenges, one Taylor Differential Incorporated Convolutional Recurrent Neural Network (TDI-CRNN) is proposed in this paper. Firstly, in order to enhance the approximation accuracy of higher-order partial differential equations and to alleviate the limitations of PDE applications, one physical module with multi-order Taylor approximation is designed. The module is firstly used for the differential approximation of the input sequence by means of the Taylor expansion, and then couples the differential convolution layers with different orders via differential coefficients, and dynamically adjusts the truncation order and the number of differential terms of the Taylor expansions. Secondly, to capture the multiple spatial scale features of the hidden states in the recurrent network and to better capture the edge spatial information of the spatiotemporal sequences, one Multi-Scale Attention Recurrent Module (MSARM) is devised. Multi-scale convolution and spatial attention mechanisms are utilized in the convolution layer of the Multi-scale Convolution Spatial Attention UNet (MCSA-UNet), aiming to focus on local spatial regions within spatiotemporal sequences. Extensive experiments are conducted on the Moving MNIST, KTH, and CIKM datasets. The Mean Squared Error (MSE) on the Moving MNIST dataset dropped to 42.7, while the Structural Similarity Index Measure (SSIM) increased to 0.912. The SSIM and Peak Signal-to-Noise Ratio (PSNR) on the KTH dataset increased to 0.882 and 29.03, respectively. The Correct Skill Index (CSI) on the real weather radar echo CIKM dataset increased to 0.515. The final visualization and quantitative prediction results verify the rationality and effectiveness of the TDI-CRNN model.
Deep spatiotemporal sequence prediction methods that incorporate a priori physical knowledge are commonly characterized by the utilization of Partial Differential Equations (PDE) for modeling. However, two main issues are concerned: (1) the limited precision in approximations with PDEs; and (2) the inability to efficiently capture spatiotemporal features at multiple spatial scales as well as the edge spatial information of the spatiotemporal sequences in the recurrent network. To address these challenges, one Taylor Differential Incorporated Convolutional Recurrent Neural Network (TDI-CRNN) is proposed in this paper. Firstly, in order to enhance the approximation accuracy of higher-order partial differential equations and to alleviate the limitations of PDE applications, one physical module with multi-order Taylor approximation is designed. The module is firstly used for the differential approximation of the input sequence by means of the Taylor expansion, and then couples the differential convolution layers with different orders via differential coefficients, and dynamically adjusts the truncation order and the number of differential terms of the Taylor expansions. Secondly, to capture the multiple spatial scale features of the hidden states in the recurrent network and to better capture the edge spatial information of the spatiotemporal sequences, one Multi-Scale Attention Recurrent Module (MSARM) is devised. Multi-scale convolution and spatial attention mechanisms are utilized in the convolution layer of the Multi-scale Convolution Spatial Attention UNet (MCSA-UNet), aiming to focus on local spatial regions within spatiotemporal sequences. Extensive experiments are conducted on the Moving MNIST, KTH, and CIKM datasets. The Mean Squared Error (MSE) on the Moving MNIST dataset dropped to 42.7, while the Structural Similarity Index Measure (SSIM) increased to 0.912. The SSIM and Peak Signal-to-Noise Ratio (PSNR) on the KTH dataset increased to 0.882 and 29.03, respectively. The Correct Skill Index (CSI) on the real weather radar echo CIKM dataset increased to 0.515. The final visualization and quantitative prediction results verify the rationality and effectiveness of the TDI-CRNN model.
2024, 46(6): 2619-2626.
doi: 10.11999/JEIT230617
Abstract:
In edge computing networks, to stimulate the Edge Computing Nodes (ECNs) to assist in computation offloading to relieve the pressure of computing Service Provider (SP), a forward transaction oriented incentive mechanism is studied. Considering that there is information asymmetry between SP and ECNs and the uncertainty of ECNs idle resources can lead to cooperation risks, a risk-aware forward incentive mechanism based on contract theory for computation offloading is proposed. Firstly, a risk preference model for nodes is established; and then the Individual Rationality (IR) constraints and Incentive Compatibility (IC) constraints are defined, and the incentive problem is modeled as a forward contract design problem to maximize the benefits of SP; finally, the optimal forward contract is derived after constraint simplification. The simulation results verify the feasibility and rationality of the proposed forward contract, and prove that the contract can effectively incentivize ECNs to participate in computation offloading and increase the profits of SP.
In edge computing networks, to stimulate the Edge Computing Nodes (ECNs) to assist in computation offloading to relieve the pressure of computing Service Provider (SP), a forward transaction oriented incentive mechanism is studied. Considering that there is information asymmetry between SP and ECNs and the uncertainty of ECNs idle resources can lead to cooperation risks, a risk-aware forward incentive mechanism based on contract theory for computation offloading is proposed. Firstly, a risk preference model for nodes is established; and then the Individual Rationality (IR) constraints and Incentive Compatibility (IC) constraints are defined, and the incentive problem is modeled as a forward contract design problem to maximize the benefits of SP; finally, the optimal forward contract is derived after constraint simplification. The simulation results verify the feasibility and rationality of the proposed forward contract, and prove that the contract can effectively incentivize ECNs to participate in computation offloading and increase the profits of SP.
2024, 46(6): 2627-2637.
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.
2024, 46(6): 2638-2646.
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.
2024, 46(6): 2647-2654.
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.
2024, 46(6): 2655-2662.
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
2024, 46(6): 2663-2672.
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 and normalized 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, and 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 and normalized 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, and the computing efficiency is increased by 8.02%～177.53% compared to other FPGA accelerators.
2024, 46(6): 2673-2680.
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.
