Email alert
Latest Articles
Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
Display Method:
Available online , doi: 10.11999/JEIT240650
Abstract:
Low Earth Orbit navigation system has a large number of satellites and Doppler frequency deviation, so the search space of the receiver with cold start is huge and the acquisition speed is slow. A Code-phase Shift Key and Linear Frequency Modulation (CSK-LFM) navigation signal waveform is proposed, the LFM modulation improves the Doppler tolerance of the signal, and the multiple access broadcast of different satellites is realized by different pseudo-code phases, which can greatly compress the three-dimensional search space of satellite number, time delay, and Doppler. Simulation and experimental results show that when the signal strength is 40dBHz, the acquisition performance of CSK-LFM signal is about 1dB higher than that of traditional Direct Sequence Spread Spectrum (DSSS) modulation navigation signals under the same conditions, and the signal search space can be reduced to 1/10 of that of DSSS modulation.
Low Earth Orbit navigation system has a large number of satellites and Doppler frequency deviation, so the search space of the receiver with cold start is huge and the acquisition speed is slow. A Code-phase Shift Key and Linear Frequency Modulation (CSK-LFM) navigation signal waveform is proposed, the LFM modulation improves the Doppler tolerance of the signal, and the multiple access broadcast of different satellites is realized by different pseudo-code phases, which can greatly compress the three-dimensional search space of satellite number, time delay, and Doppler. Simulation and experimental results show that when the signal strength is 40dBHz, the acquisition performance of CSK-LFM signal is about 1dB higher than that of traditional Direct Sequence Spread Spectrum (DSSS) modulation navigation signals under the same conditions, and the signal search space can be reduced to 1/10 of that of DSSS modulation.
Available online , doi: 10.11999/JEIT240663
Abstract:
Covert communication is considered an important branch in the field of network security, which allows for secure data transmission in monitored environments. However, challenges such as complex communication environments and wide coverage areas are encountered in practical communication systems, making the deployment of covert communication difficult. To address this issue, a wireless covert communication system assisted by Intelligent Reflective Surfaces (IRS) and Unmanned Aerial Vehicle (UAV) is proposed in this paper. In this system, IRS is introduced as relay node to forward signals from the transmitter. UAV is utilized as a friendly node for the transmitter, and artificial noise is transmitted to disrupt malicious users’ detection of covert communication. Under the condition of receiver uncertainty regarding the received noise, the minimum error detection probability is derived, and the optimization problem of the system is established with the objective of maximizing the covert communication rate while considering interruption probability as a constraint. The Dinkelbach algorithm is employed to solve the optimization problem. Simulation results demonstrate that the maximum covert communication rate can be achieved when the phase shift of the IRS elements and the UAV’s transmission power are optimized.
Covert communication is considered an important branch in the field of network security, which allows for secure data transmission in monitored environments. However, challenges such as complex communication environments and wide coverage areas are encountered in practical communication systems, making the deployment of covert communication difficult. To address this issue, a wireless covert communication system assisted by Intelligent Reflective Surfaces (IRS) and Unmanned Aerial Vehicle (UAV) is proposed in this paper. In this system, IRS is introduced as relay node to forward signals from the transmitter. UAV is utilized as a friendly node for the transmitter, and artificial noise is transmitted to disrupt malicious users’ detection of covert communication. Under the condition of receiver uncertainty regarding the received noise, the minimum error detection probability is derived, and the optimization problem of the system is established with the objective of maximizing the covert communication rate while considering interruption probability as a constraint. The Dinkelbach algorithm is employed to solve the optimization problem. Simulation results demonstrate that the maximum covert communication rate can be achieved when the phase shift of the IRS elements and the UAV’s transmission power are optimized.
Available online , doi: 10.11999/JEIT240624
Abstract:
The task offloading algorithm based on single-agent reinforcement learning encounters strategy degradation issues due to the mutual influence between agents when addressing task offloading in large-scale Multi-access Edge Computing (MEC) systems. In contrast, traditional multi-agent algorithms, such as the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) suffer from poor scalability as the dimensions of the joint action space increase proportionally with the number of agents in the system. To address these issues, the large-scale MEC task offloading problem is modeled as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on mean-field multi-agent reinforcement learning is proposed. The introduction of a Long Short-Term Memory (LSTM) network addresses the partial observability problem, while mean-field approximation theory reduces the dimensionality of the joint action space. Simulation results demonstrate that the proposed algorithm outperforms single-agent task offloading algorithms in terms of task delay and task drop rate. Furthermore, even with reduced dimensions of the joint action space, the algorithm maintains performance in terms of task delay and task drop rate consistent with MADDPG.
The task offloading algorithm based on single-agent reinforcement learning encounters strategy degradation issues due to the mutual influence between agents when addressing task offloading in large-scale Multi-access Edge Computing (MEC) systems. In contrast, traditional multi-agent algorithms, such as the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) suffer from poor scalability as the dimensions of the joint action space increase proportionally with the number of agents in the system. To address these issues, the large-scale MEC task offloading problem is modeled as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on mean-field multi-agent reinforcement learning is proposed. The introduction of a Long Short-Term Memory (LSTM) network addresses the partial observability problem, while mean-field approximation theory reduces the dimensionality of the joint action space. Simulation results demonstrate that the proposed algorithm outperforms single-agent task offloading algorithms in terms of task delay and task drop rate. Furthermore, even with reduced dimensions of the joint action space, the algorithm maintains performance in terms of task delay and task drop rate consistent with MADDPG.
Available online , doi: 10.11999/JEIT240308
Abstract:
Objective Inverse Lithography Technology (ILT) provides improved imaging effects and a larger process window compared to traditional Optical Proximity Correction (OPC). As chip manufacturing continually reduces process dimensions, ILT has become the leading lithography mask correction technology. This paper first introduces the basic principles and several common implementation methods of the reverse lithography algorithm. It then reviews current research on using reverse lithography technology to optimize lithography masks, as well as analyzes the advantages and existing challenges of this technology. Methods The general process of generating mask patterns in ILT is exemplified using the level set method. First, the target graphics, light sources, and other inputs are identified. Then, the initial mask pattern is created and a pixelated model is constructed. A photolithography model is then established to calculate the aerial image. The general photoresist threshold model is represented by a sigmoid function, which helps derive the imaging pattern on the photoresist. The key element of the ILT algorithm is the cost function, which measures the difference between the wafer image and the target image. The optimization direction is determined based on the chosen cost function. For instance, the continuous cost function can calculate gradients, enabling the use of gradient descent to find the optimal solution. Finally, when the cost function reaches its minimum, the output mask is generated. Results and Discussions This paper systematically introduces several primary methods for implementing ILT. The level set method's main concept is to convert a two-dimensional closed curve into a three-dimensional surface. Here, the closed curve is viewed as the set of intersection lines between the surface and the zero plane. During the ILT optimization process, the three-dimensional surface shape remains continuous. This continuity allows the ILT problem to be transformed into a multivariate optimization problem, solvable using gradient algorithms, machine learning, and other methods. Examples of the level set method's application can be found in both mask optimization and light source optimization. The level set mathematical framework effectively addresses two-dimensional curve evolution. When designing the ILT algorithm, a lithography model determines the optimization direction and velocity for each mask point, employing the level set for mask evolution. Intel has proposed an algorithm that utilizes a pixelated model to optimize the entire chip. However, this approach incurs significant computational costs, necessitating larger mask pixel sizes. Notably, the pixelated model is consistently used throughout the process, with a defined pixelated cost function applicable to multi-color masks. The frequency domain method for calculating the ILT curve involves transforming the mask from the spatial domain into the frequency domain, followed by lithography model calculations. This approach generates a mask with continuous pixel values, which is then gradually converted into a binary mask through multiple steps. When modifying the cost function in frequency domain optimization, all symmetric and repetitive patterns are altered uniformly, preserving symmetry. The transition of complex convolution calculations into multiplication processes within the frequency domain significantly reduces computational complexity and can be accelerated using GPU technology. Due to the prevalent issue of high computational complexity in various lithography mask optimization algorithms, scholars have long pursued machine learning solutions for mask optimization. Early research often overlooked the physical model of photolithography technology, training neural networks solely based on optimized mask features. This oversight led to challenges such as narrow process windows. Recent studies have, however, integrated machine learning with other techniques, enabling the physical model of lithography technology to influence neural network training, resulting in improved optimization results. While the ILT-optimized mask lithography process window is relatively large, its high computational complexity limits widespread application. Therefore, combining machine learning with the ILT method represents a promising research direction. Conclusions Three primary techniques exist for optimizing masks using ILT: the Level Set Method, Intel Pixelated ILT Method, and Frequency Domain Calculation of Curve ILT. The Level Set Method reformulates the ILT challenge into a multivariate optimization issue, utilizing a continuous cost function. This approach allows for the application of established methods like gradient descent, which has attracted significant attention and is well-documented in the literature. In contrast, the Intel method relies entirely on pixelated models and pixelated cost functions, though relevant literature on this method is limited. Techniques in the frequency domain can leverage GPU operations to substantially enhance computational speed, and advanced algorithms also exist for converting curve masks into Manhattan masks. The integration of ILT with machine learning technologies shows considerable potential for development. Further research is necessary to effectively reduce computational complexity while ensuring optimal results. Currently, ILT technology faces challenges such as high computational demands and obstacles in full layout optimization. Collaboration among experts and scholars in integrated circuit design and related fields is essential to improve ILT computational speed and to integrate it with other technologies. We believe that as research on ILT-related technologies advances, it will play a crucial role in helping China's chip industry overcome technological bottlenecks in the future.
Available online , doi: 10.11999/JEIT240648
Abstract:
Objective Rotating machinery is essential across various industrial sectors, including energy, aerospace, and manufacturing. However, these machines operate under complex and variable conditions, making timely and accurate fault detection a significant challenge. Traditional diagnostic methods, which use a single sensor and modality, often miss critical features, particularly subtle fault signatures. This can result in reduced reliability, increased downtime, and higher maintenance costs. To address these issues, this study proposes a novel modal fusion deep clustering approach for multi-sensor fault diagnosis in rotating machinery. The main objectives are to: (1) improve feature extraction through time-frequency transformations that reveal important temporal-spectral patterns, (2) implement an attention-based modality fusion strategy that integrates complementary information from various sensors, and (3) use a deep clustering framework to identify fault types without needing labeled training data. Methods The proposed approach utilizes a multi-stage pipeline for thorough feature extraction and analysis. First, raw multi-sensor signals, such as vibration data collected under different load and speed conditions, are preprocessed and transformed with the Short-Time Fourier Transform (STFT). This converts time-domain signals into time-frequency representations, highlighting distinct frequency components related to various fault conditions. Next, Gated Recurrent Units (GRUs) model temporal dependencies and capture long-range correlations, while Convolutional Autoencoders (CAEs) learn hierarchical spatial features from the transformed data. By combining GRUs and CAEs, the framework encodes both temporal and structural patterns, creating richer and more robust representations than traditional methods that rely solely on either technique or handcrafted features. A key innovation is the modality fusion attention mechanism. In multi-sensor environments, individual sensors typically capture complementary aspects of system behavior. Simply concatenating their outputs can lead to suboptimal results due to noise and irrelevant information. The proposed attention-based fusion calculates modality-specific affinity matrices to assess the relationship and importance of each sensor modality. With learnable attention weights, the framework prioritizes the most informative modalities while diminishing the impact of less relevant ones. This ensures the fused representation captures complementary information, resulting in improved discriminative power. Finally, an unsupervised clustering module is integrated into the deep learning pipeline. Rather than depending on labeled data, the model assigns samples to clusters by refining cluster assignments iteratively using a Kullback-Leibler (KL) divergence-based objective. Initially, a soft cluster distribution is created from the learned features. A target distribution is then computed to sharpen and define cluster boundaries. By continuously minimizing the KL divergence between these distributions, the model self-optimizes over time, producing well-separated clusters corresponding to distinct fault types without supervision. Results and Discussions The proposed approach’s effectiveness is illustrated using multi-sensor bearing and gearbox datasets. Compared to conventional unsupervised methods—like traditional clustering algorithms or single-domain feature extraction techniques—this framework significantly enhances clustering accuracy and fault recognition rates. Experimental results show recognition accuracies of approximately 99.16% on gearbox data and 98.63% on bearing data, representing a notable advancement over existing state-of-the-art techniques. These impressive results stem from the synergistic effects of advanced feature extraction, modality fusion, and iterative clustering refinement. By extracting time-frequency features through STFT, the method captures a richer representation than relying solely on raw time-domain signals. The use of GRUs incorporates temporal information, enabling the capture of dynamic signal changes that may indicate evolving fault patterns. Additionally, CAEs reveal meaningful spatial structures from time-frequency data, resulting in low-dimensional yet highly informative embeddings. The modality fusion attention mechanism further enhances these benefits by emphasizing relevant modalities, such as vibration data from various sensor placements or distinct physical principles, thus leveraging their complementary strengths. Through the iterative minimization of KL divergence, the clustering process becomes more discriminative. Initially broad and overlapping cluster boundaries are progressively refined, allowing the model to converge toward stable and well-defined fault groupings. This unsupervised approach is particularly valuable in practical scenarios, where obtaining labeled data is costly and time-consuming. The model’s ability to learn directly from unlabeled signals enables continuous monitoring and adaptation, facilitating timely interventions and reducing the risk of unexpected machine failures. The discussion emphasizes the adaptability of the proposed method. Industrial systems continuously evolve, and fault patterns can change over time due to aging, maintenance, or shifts in operational conditions. The unsupervised method can be periodically retrained or updated with new unlabeled data. This allows it to monitor changes in machinery health and quickly detect new fault conditions without the need for manual annotation. Additionally, the attention-based modality fusion is flexible enough to support the inclusion of new sensor types or measurement channels, potentially enhancing diagnostic performance as richer data sources become available. Conclusions This study presents a modal fusion deep clustering framework designed for the multi-sensor fault diagnosis of rotating machinery. By combining time-frequency transformations with GRU- and CAE-based deep feature encoders, attention-driven modality fusion, and KL divergence-based unsupervised clustering, this approach outperforms traditional methods in accuracy, robustness, and scalability. Key contributions include a comprehensive multi-domain feature extraction pipeline, an adaptive modality fusion strategy for heterogeneous sensor data integration, and a refined deep clustering mechanism that achieves high diagnostic accuracy without relying on labeled training samples. Looking ahead, there are several promising directions. Adding more modalities—like acoustic emissions, temperature signals, or electrical measurements—could lead to richer feature sets. Exploring semi-supervised or few-shot extensions may further enhance performance by utilizing minimal labeled guidance when available. Implementing the proposed model in an industrial setting, potentially for real-time use, would also validate its practical benefits for maintenance decision-making, helping to reduce operational costs and extend equipment life.
Available online , doi: 10.11999/JEIT240595
Abstract:
In the complex Marine environment, the known information of the target is seriously disturbed by environmental noise and reverberation, which leads to poor target tracking effect, and it is difficult to extract the utilizable features of the target from these disturbances. This paper proposes an improved extended Kalman filter tracking method based on active waveguide invariant distribution by integrating the coupling characteristics of the target and environment into the target tracking algorithm. Firstly, based on the basic theory of target scattering in shallow sea waveguides, the mathematical model of invariant representation of active waveguide under the condition of receiving and receiving separation is derived, and the constraint relation of distance, frequency, and invariant distribution of active waveguide is obtained. Then this constraint is added to the state vector of the extended Kalman filter, and the fit degree between the model and the real trajectory of the target is improved by adding new constraints to enhance the precision of target tracking. Finally, the tracking performance of the proposed method is verified by simulation experiments and measured data. The results show that: compared with the conventional extended Kalman filter tracking method, the proposed method can improve the tracking accuracy of the target better. The optimization rate of the simulation results can reach about 50%, and the optimization rate of the measured data processing results is about 60%.
In the complex Marine environment, the known information of the target is seriously disturbed by environmental noise and reverberation, which leads to poor target tracking effect, and it is difficult to extract the utilizable features of the target from these disturbances. This paper proposes an improved extended Kalman filter tracking method based on active waveguide invariant distribution by integrating the coupling characteristics of the target and environment into the target tracking algorithm. Firstly, based on the basic theory of target scattering in shallow sea waveguides, the mathematical model of invariant representation of active waveguide under the condition of receiving and receiving separation is derived, and the constraint relation of distance, frequency, and invariant distribution of active waveguide is obtained. Then this constraint is added to the state vector of the extended Kalman filter, and the fit degree between the model and the real trajectory of the target is improved by adding new constraints to enhance the precision of target tracking. Finally, the tracking performance of the proposed method is verified by simulation experiments and measured data. The results show that: compared with the conventional extended Kalman filter tracking method, the proposed method can improve the tracking accuracy of the target better. The optimization rate of the simulation results can reach about 50%, and the optimization rate of the measured data processing results is about 60%.
Available online , doi: 10.11999/JEIT240469
Abstract:
An adaptive target tracking method based on marginalized cubature Kalman filter is proposed for the target tracking problem in the presence of sensor measurement biases and unknown time-varying measurement noise. In this method, the constant measurement biases are eliminated by measurement differencing and the abrupt measurement biases can be identified by constructing the beta-Bernoulli indicator variable. The target states at adjacent moments are augmented to meet real-time filtering. The inverse Wishart distribution is used to model the covariance matrix of unknown measurement noise. Consequently, the joint distribution of the target state, indicator variable, and noise covariance matrix can be established, and the approximate posterior of each parameter is solved by variational Bayesian inference. To reduce the computational burden, the augmented state vector is marginalized, and the target tracking based marginal cubature Kalman filter is realized by combining with cubature Kalman filter. Simulation results demonstrate that the proposed method effectively handles the abrupt measurement biases and unknown time-varying measurement noise, achieving accurate target tracking.
An adaptive target tracking method based on marginalized cubature Kalman filter is proposed for the target tracking problem in the presence of sensor measurement biases and unknown time-varying measurement noise. In this method, the constant measurement biases are eliminated by measurement differencing and the abrupt measurement biases can be identified by constructing the beta-Bernoulli indicator variable. The target states at adjacent moments are augmented to meet real-time filtering. The inverse Wishart distribution is used to model the covariance matrix of unknown measurement noise. Consequently, the joint distribution of the target state, indicator variable, and noise covariance matrix can be established, and the approximate posterior of each parameter is solved by variational Bayesian inference. To reduce the computational burden, the augmented state vector is marginalized, and the target tracking based marginal cubature Kalman filter is realized by combining with cubature Kalman filter. Simulation results demonstrate that the proposed method effectively handles the abrupt measurement biases and unknown time-varying measurement noise, achieving accurate target tracking.
Available online , doi: 10.11999/JEIT240735
Abstract:
To improve the performance of object segmentation without increasing the quantity of parameters, a self-distillation object segmentation method based on Transformer feature pyramid is proposed, which enhances the utility of Transformer segmentation model. First, a pixel-wise object segmentation model is constructed using Swin Transformer as the backbone network. Then, the auxiliary branch is designed as a combination of Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), Adjacent Feature Fusion Modules (AFFM) and the scoring module, which guide the main network through self-distillation. Finally, a top-down learning strategy is used to guide model learning to ensure consistency in self-distillation. The experiments on four famous public datasets show that the proposed method can effectively improve the accuracy of object segmentation on four public datasets, with a near 1.6% increase in Fβ compared to the Transformer Knowledge Distillation (TKD) method on the Camouflage Object Detection (COD) dataset.
To improve the performance of object segmentation without increasing the quantity of parameters, a self-distillation object segmentation method based on Transformer feature pyramid is proposed, which enhances the utility of Transformer segmentation model. First, a pixel-wise object segmentation model is constructed using Swin Transformer as the backbone network. Then, the auxiliary branch is designed as a combination of Densely connected Atrous Spatial Pyramid Pooling (DenseASPP), Adjacent Feature Fusion Modules (AFFM) and the scoring module, which guide the main network through self-distillation. Finally, a top-down learning strategy is used to guide model learning to ensure consistency in self-distillation. The experiments on four famous public datasets show that the proposed method can effectively improve the accuracy of object segmentation on four public datasets, with a near 1.6% increase in Fβ compared to the Transformer Knowledge Distillation (TKD) method on the Camouflage Object Detection (COD) dataset.
Available online , doi: 10.11999/JEIT240640
Abstract:
Integrated Sensing And Communication (ISAC) as a key technology for 6G integrates communication and sensing functions into Wi-Fi devices, providing an effective method for indoor human breath rate sensing. Addressing current challenges of low robustness and blind spots in ISAC-based breath rate sensing, a Variational Mode Decomposition (VMD)-Hilbert-Huang Transform (HHT) based algorithm for breath rate sensing is proposed in this paper. First, Wi-Fi links with high environmental sensitivity are selected to construct the Channel State Information (CSI) ratio model. Then, the subcarriers of the filtered CSI ratio time series are projected, and amplitude and phase information are combined to generate a candidate set of different breathing mode signals. Next, for each subcarrier, the candidate sequence with the highest short-term breath noise ratio is selected as the final breath pattern based on periodicity in the candidates. Then, a threshold is set to select subcarriers, followed by performing time-frequency analysis using VMD and HHT to remove modal components other than the human breath frequency, and reconstructing the remaining modal components. Subsequently, Principal Component Analysis (PCA) is employed to reduce the dimensionality of all reconstructed subcarriers, selecting principal components that contribute over 99% of the variance. The ReliefF algorithm is then used to reconstruct the breath signal as a fused signal. Finally, the breath rate is calculated using a peak detection algorithm on the fused signal. Experimental results show that the proposed detection method achieves the mean estimation accuracy of over 97% in both conference room and corridor scenarios, significantly enhancing robustness and overcoming the "blind spots" problem, outperforming other existing detection schemes.
Integrated Sensing And Communication (ISAC) as a key technology for 6G integrates communication and sensing functions into Wi-Fi devices, providing an effective method for indoor human breath rate sensing. Addressing current challenges of low robustness and blind spots in ISAC-based breath rate sensing, a Variational Mode Decomposition (VMD)-Hilbert-Huang Transform (HHT) based algorithm for breath rate sensing is proposed in this paper. First, Wi-Fi links with high environmental sensitivity are selected to construct the Channel State Information (CSI) ratio model. Then, the subcarriers of the filtered CSI ratio time series are projected, and amplitude and phase information are combined to generate a candidate set of different breathing mode signals. Next, for each subcarrier, the candidate sequence with the highest short-term breath noise ratio is selected as the final breath pattern based on periodicity in the candidates. Then, a threshold is set to select subcarriers, followed by performing time-frequency analysis using VMD and HHT to remove modal components other than the human breath frequency, and reconstructing the remaining modal components. Subsequently, Principal Component Analysis (PCA) is employed to reduce the dimensionality of all reconstructed subcarriers, selecting principal components that contribute over 99% of the variance. The ReliefF algorithm is then used to reconstruct the breath signal as a fused signal. Finally, the breath rate is calculated using a peak detection algorithm on the fused signal. Experimental results show that the proposed detection method achieves the mean estimation accuracy of over 97% in both conference room and corridor scenarios, significantly enhancing robustness and overcoming the "blind spots" problem, outperforming other existing detection schemes.
Available online , doi: 10.11999/JEIT240590
Abstract:
In response to the temporary and emergent issue of poor communication in rural and remote areas, an adaptive multi-Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing And Communication (ISAC) mechanism is proposed in this paper. In scenarios where ground users and sensing targets are randomly distributed in clusters, the mechanism achieves comprehensive communication coverage by rationally scheduling multiple UAVs, providing a novel solution and scheme for UAV-enabled ISAC systems. The spatial deployment of UAVs and their beamforming directed towards ground equipment are primarily addressed in this paper. Under the constraints of the air-ground association policy, the system can maximize the lower bound of the users’ transmission reachable rate by optimizing the set of communication and sensing beamforming variables for the UAVs, while ensuring the basic requirements of ISAC. To solve the considered non-convex optimization problems, the Mean Shift (MS) algorithm based on Gaussian kernels to manage the mixed-integer linear issues within the association strategy is first employed. Additionally, combining the quadratic transformation and Successive Convex Approximation (SCA), the optimization of beamforming is conducted via the Block Coordinate Descent (BCD) method, thereby securing a suboptimal solution. Numerical results validate the effectiveness of the adaptive mechanism.
In response to the temporary and emergent issue of poor communication in rural and remote areas, an adaptive multi-Unmanned Aerial Vehicle (UAV)-assisted Integrated Sensing And Communication (ISAC) mechanism is proposed in this paper. In scenarios where ground users and sensing targets are randomly distributed in clusters, the mechanism achieves comprehensive communication coverage by rationally scheduling multiple UAVs, providing a novel solution and scheme for UAV-enabled ISAC systems. The spatial deployment of UAVs and their beamforming directed towards ground equipment are primarily addressed in this paper. Under the constraints of the air-ground association policy, the system can maximize the lower bound of the users’ transmission reachable rate by optimizing the set of communication and sensing beamforming variables for the UAVs, while ensuring the basic requirements of ISAC. To solve the considered non-convex optimization problems, the Mean Shift (MS) algorithm based on Gaussian kernels to manage the mixed-integer linear issues within the association strategy is first employed. Additionally, combining the quadratic transformation and Successive Convex Approximation (SCA), the optimization of beamforming is conducted via the Block Coordinate Descent (BCD) method, thereby securing a suboptimal solution. Numerical results validate the effectiveness of the adaptive mechanism.
Available online , doi: 10.11999/JEIT240012
Abstract:
The Integrated Sensing And Communication (ISAC) requires that communication and sensing share the same radio frequency band and hardware resource. The characteristics of multi bands, large bandwidth, communication and sensing’s different requirements for hardware put forward higher requirements for ISAC hardware design. The hardware designs, verification technologies and systemic hardware verification platforms of beyond 5G, 6G and WiFi ISACs are summarized. The relevant hardware designs and verification researches at home and abroad in recent years are summarized also. The hardware design challenges such as the hardware requirement contradictions between communication and sensing systems, the In Band Full Duplex (IBFD) Self-Interference Cancellation (SIC), the Power Amplifier (PA) efficiency, and the more accurate modeling required by circuit performance are paid attention to. First of all, the design of ISAC transceiver architectures in existing researches are summarized and compared. Then, the existing ISAC IBFD self-interference suppression schemes, the low Peak to Average Power Ratio (PAPR) waveform or high-performance PA designs, the high precision device modeling methods and the systemic hardware verification platforms are introduced and analyzed. At last, the full text is summarized, the future open issues for ISAC hardware design are analyzed.
The Integrated Sensing And Communication (ISAC) requires that communication and sensing share the same radio frequency band and hardware resource. The characteristics of multi bands, large bandwidth, communication and sensing’s different requirements for hardware put forward higher requirements for ISAC hardware design. The hardware designs, verification technologies and systemic hardware verification platforms of beyond 5G, 6G and WiFi ISACs are summarized. The relevant hardware designs and verification researches at home and abroad in recent years are summarized also. The hardware design challenges such as the hardware requirement contradictions between communication and sensing systems, the In Band Full Duplex (IBFD) Self-Interference Cancellation (SIC), the Power Amplifier (PA) efficiency, and the more accurate modeling required by circuit performance are paid attention to. First of all, the design of ISAC transceiver architectures in existing researches are summarized and compared. Then, the existing ISAC IBFD self-interference suppression schemes, the low Peak to Average Power Ratio (PAPR) waveform or high-performance PA designs, the high precision device modeling methods and the systemic hardware verification platforms are introduced and analyzed. At last, the full text is summarized, the future open issues for ISAC hardware design are analyzed.
Available online , doi: 10.11999/JEIT240601
Abstract:
Due to the sharing of communication and sensing waveforms, Integrated Sensing And Communication (ISAC) systems are more vulnerable to the risk of information leakage. The Movable Element Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (ME-STAR-RIS) assisted ISAC system from the perspective of covert communication is investigated in this paper. The ME-STAR-RIS array elements can be moved within a certain range to obtain more favorable channel conditions. Based on the discrete element position model, the joint beamforming optimization problem is formulated which aims to jointly design the active beamforming at the ISAC Base Station (BS) and the flexible passive beamforming (including array element positions, phase shifts, and amplitude coefficients) at the ME-STAR-RIS to maximize the probing beam gain at the sensing target within covert communication quality constraints. A two-layer iterative algorithm is proposed to efficiently solve the active and flexible passive beamforming problem. The simulation results verify the effectiveness of the proposed algorithm and show that by moving the elements, a narrower and stronger detection beam can be obtained, which is conducive to improving the system’s performance.
Due to the sharing of communication and sensing waveforms, Integrated Sensing And Communication (ISAC) systems are more vulnerable to the risk of information leakage. The Movable Element Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (ME-STAR-RIS) assisted ISAC system from the perspective of covert communication is investigated in this paper. The ME-STAR-RIS array elements can be moved within a certain range to obtain more favorable channel conditions. Based on the discrete element position model, the joint beamforming optimization problem is formulated which aims to jointly design the active beamforming at the ISAC Base Station (BS) and the flexible passive beamforming (including array element positions, phase shifts, and amplitude coefficients) at the ME-STAR-RIS to maximize the probing beam gain at the sensing target within covert communication quality constraints. A two-layer iterative algorithm is proposed to efficiently solve the active and flexible passive beamforming problem. The simulation results verify the effectiveness of the proposed algorithm and show that by moving the elements, a narrower and stronger detection beam can be obtained, which is conducive to improving the system’s performance.
Available online , doi: 10.11999/JEIT250000
Abstract:
In this report, the application and funding statistics of several projects in the electronics and technology area under Division I of Information Science Department of the National Natural Science Foundation of China in 2024 are summarized . These projects include key program, general program, young scientists fund, fund for less developed regions, excellent young scientists fund and national science fund for distinguished young scholars. Their distribution characteristics and hot topics are sorted out from application codes, the age of applicants, the changes in the past five or ten years. Through the above analysis, it is intended to provide references for the researchers to understand the research directions that need to be strengthened and the impact of some reform measures on the application and funding of projects in this field.
In this report, the application and funding statistics of several projects in the electronics and technology area under Division I of Information Science Department of the National Natural Science Foundation of China in 2024 are summarized . These projects include key program, general program, young scientists fund, fund for less developed regions, excellent young scientists fund and national science fund for distinguished young scholars. Their distribution characteristics and hot topics are sorted out from application codes, the age of applicants, the changes in the past five or ten years. Through the above analysis, it is intended to provide references for the researchers to understand the research directions that need to be strengthened and the impact of some reform measures on the application and funding of projects in this field.
Available online , doi: 10.11999/JEIT240659
Abstract:
To meet the latency and energy consumption requirements of Internet of Things(IoT) devices in remote and disaster areas, a new dynamic satellite-assisted IoT network model consisting of IoT devices, Low Earth orbit(LEO) satellites, and a cloud computing center is constructed. Under the practical constraints such as latency, energy consumption etc, the weighted sum of system latency and energy consumption is regarded as cost, and the joint task offloading and power and computational resource allocation problem for minimizing system cost is constructed. For dynamic task arrival scenarios, a Model-assisted Adaptive Deep Reinforcement Learning (MADRL) algorithm is proposed for the joint allocation of task-offloading decisions, communication and computational resources. The algorithm solves the problem in two parts, the first part optimizes the communication, and computational resources through model-assisted, binary search algorithms and gradient descent method, and the second part trains the Q-network to adapt to random task arrivals and make optimal offloading decisions through adaptive deep reinforcement learning algorithms. Simulation results show that the introduction of satellite mobility reduces the system cost by 41%. The introduction of the inter-satellite cooperation technique reduces the system cost by 22.1%. Moreover, the algorithm proposed in this paper has good convergence performance, which reduces the system cost by 3% compared to the benchmark algorithm, and has optimal performance in different environments.
To meet the latency and energy consumption requirements of Internet of Things(IoT) devices in remote and disaster areas, a new dynamic satellite-assisted IoT network model consisting of IoT devices, Low Earth orbit(LEO) satellites, and a cloud computing center is constructed. Under the practical constraints such as latency, energy consumption etc, the weighted sum of system latency and energy consumption is regarded as cost, and the joint task offloading and power and computational resource allocation problem for minimizing system cost is constructed. For dynamic task arrival scenarios, a Model-assisted Adaptive Deep Reinforcement Learning (MADRL) algorithm is proposed for the joint allocation of task-offloading decisions, communication and computational resources. The algorithm solves the problem in two parts, the first part optimizes the communication, and computational resources through model-assisted, binary search algorithms and gradient descent method, and the second part trains the Q-network to adapt to random task arrivals and make optimal offloading decisions through adaptive deep reinforcement learning algorithms. Simulation results show that the introduction of satellite mobility reduces the system cost by 41%. The introduction of the inter-satellite cooperation technique reduces the system cost by 22.1%. Moreover, the algorithm proposed in this paper has good convergence performance, which reduces the system cost by 3% compared to the benchmark algorithm, and has optimal performance in different environments.
Available online , doi: 10.11999/JEIT240029
Abstract:
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit. Furthermore is constructed, under the premise of analyzing the defense-offense strategies of game models. A topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model, and the topology deception defense-offense scenarios in cloud-native networks is analyzed. It is shown that the algorithm has significant advantages compared to other algorithms.
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments, a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks. Firstly, the topology deception defense-offense model in cloud-native complex network environments is analyzed. Then, by introducing a discount factor and transition probabilities, a multi-stage game-based network topology deception model based on Flipit. Furthermore is constructed, under the premise of analyzing the defense-offense strategies of game models. A topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models. Finally, through experiments, it is demonstrated that the proposed method can effectively model, and the topology deception defense-offense scenarios in cloud-native networks is analyzed. It is shown that the algorithm has significant advantages compared to other algorithms.
Available online , doi: 10.11999/JEIT240087
Abstract:
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
In order to improve the accuracy of emotion recognition models and solve the problem of insufficient emotional feature extraction, this paper conducts research on bimodal emotion recognition involving audio and facial imagery. In the audio modality, a feature extraction model of a Multi-branch Convolutional Neural Network (MCNN) incorporating a channel-space attention mechanism is proposed, which extracts emotional features from speech spectrograms across time, space, and local feature dimensions. For the facial image modality, a feature extraction model using a Residual Hybrid Convolutional Neural Network (RHCNN) is introduced, which further establishes a parallel attention mechanism that concentrates on global emotional features to enhance recognition accuracy. The emotional features extracted from audio and facial imagery are then classified through separate classification layers, and a decision fusion technique is utilized to amalgamate the classification results. The experimental results indicate that the proposed bimodal fusion model has achieved recognition accuracies of 97.22%, 94.78%, and 96.96% on the RAVDESS, eNTERFACE’05, and RML datasets, respectively. These accuracies signify improvements over single-modality audio recognition by 11.02%, 4.24%, and 8.83%, and single-modality facial image recognition by 4.60%, 6.74%, and 4.10%, respectively. Moreover, the proposed model outperforms related methodologies applied to these datasets in recent years. This illustrates that the advanced bimodal fusion model can effectively focus on emotional information, thereby enhancing the overall accuracy of emotion recognition.
Available online , doi: 10.11999/JEIT240399
Abstract:
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Wireless through-the-earth communication provides a solution for information transmission in heavily shielded space. The received current field signal has low Signal-to-Noise Ratio (SNR), is easily distorted, and is greatly affected by carrier frequency offset, making signal acquisition difficult. In this paper, a long synchronization signal frame structure is designed and a two-stage long correlation signal acquisition algorithm is proposed that combines coarse and fine frequency offset estimation. In the first stage, the training symbols in the received time-domain signal are used for coarse estimation of sampling interval deviation based on the maximum likelihood algorithm, and the coarse estimation value of the sampling point compensation interval is calculated. In the second stage, the coarse estimation value and the received SNR are combined to determine the traversal range of the fine estimation value of the sampling point compensation interval. A long correlation template signal with local compensation is designed to achieve accurate acquisition of the current field signal. The algorithm’s performance is verified in a heavily shielded space located 30.26 m below the ground. Experimental results show that compared to traditional sliding correlation algorithms, the proposed algorithm has a higher acquisition success probability.
Available online , doi: 10.11999/JEIT240299
Abstract:
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, System-level Exploration and Evaluation simulator for Chiplet (SEEChiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system is designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level Cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
As Moore’s Law comes to an end, it is more and more difficult to improve the chip manufacturing process, and chiplet technology has been widely adopted to improve the chip performance. However, new design parameters introduced into the chiplet architecture pose significant challenges to the computer architecture simulator. To fully support exploration and evaluation of chiplet architecture, System-level Exploration and Evaluation simulator for Chiplet (SEEChiplet), a framework based on gem5 simulator, is developed in this paper. Firstly, three design parameters concerned about chiplet chip design are summarized in this paper, including: (1) chiplet cache system design; (2) Packaging simulation; (3) Interconnection networks between chiplet. Secondly, in view of the above three design parameters, in this paper: (1) a new private last level cache system is designed and implemented to expand the cache system design space; (2) existing gem5 global directory is modified to adapt to new private Last Level Cache (LLC) system; (3) two common packaging methods of chiplet and inter-chiplet network are modeled. Finally, a chiplet-based processor is simulated with PARSEC 3.0 benchmark program running on it, which proves that SEEChiplet can explore and evaluate the design space of chiplet.
Available online , doi: 10.11999/JEIT240407
Abstract:
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
The application of Deep Reinforcement Learning (DRL) in intelligent driving decision-making is increasingly widespread, as it effectively enhances decision-making capabilities through continuous interaction with the environment. However, DRL faces challenges in practical applications due to low learning efficiency and poor data-sharing security. To address these issues, a Directed Acyclic Graph (DAG)blockchain-assisted deep reinforcement learning Intelligent Driving Strategy Optimization (D-IDSO) algorithm is proposed. First, a dual-layer secure data-sharing architecture based on DAG blockchain is constructed to ensure the efficiency and security of model data sharing. Next, a DRL-based intelligent driving decision model is designed, incorporating a multi-objective reward function that optimizes decision-making by jointly considering safety, comfort, and efficiency. Additionally, an Improved Prioritized Experience Replay with Twin Delayed Deep Deterministic policy gradient (IPER-TD3) method is proposed to enhance training efficiency. Finally, braking and lane-changing scenarios are selected in the CARLA simulation platform to train Connected and Automated Vehicles (CAVs). Experimental results demonstrate that the proposed algorithm significantly improves model training efficiency in intelligent driving scenarios, while ensuring data security and enhancing the safety, comfort, and efficiency of intelligent driving.
A SAR Image Aircraft Target Detection and Recognition Network with Target Region Feature Enhancement
Available online , doi: 10.11999/JEIT240491
Abstract:
In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
In Synthetic Aperture Radar (SAR) image aircraft target detection and recognition, the discrete characteristics of aircraft target images and the similarity between structures can reduce the accuracy of aircraft detection and recognition. A SAR image aircraft target detection and recognition network with enhanced target area features is proposed in this paper. The network consists of three parts: Feature Protecting Cross Stage Partial Darknet (FP-CSPDarnet) for protecting aircraft features, Feature Pyramid Net with Adaptive fusion (FPN-A) for adaptive feature fusion, and Detection Head for target area scattering feature extraction and enhancement (D-Head). FP-CSPDarnet can effectively protect the aircraft features in SAR images while extracting features; FPN-A adopts multi-level feature adaptive fusion and refinement to enhance aircraft features; D-Head effectively enhances the identifiable features of the aircraft before detection, improving the accuracy of aircraft detection and recognition. The experimental results using the SAR-ADRD dataset have demonstrated the effectiveness of the proposed method, with an average accuracy improvement of 2.0% compared to the baseline network YOLOv5s.
Research on Combined Navigation Algorithm Based on Adaptive Interactive Multi-Kalman Filter Modeling
Available online , doi: 10.11999/JEIT240426
Abstract:
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
Practical applications struggle to obtain prior knowledge about inertial systems and sensors, affecting information fusion and positioning accuracy in combined navigation systems. To address the degradation of integrated navigation performance due to satellite signal quality changes and system nonlinearity in vehicle navigation, a Fuzzy Adaptive Interactive Multi-Model algorithm based on Multiple Kalman Filters (FAIMM-MKF) is proposed. It integrates a Fuzzy Controller based on satellite signal quality (Fuzzy Controller) and an Adaptive Interactive Multi-Model (AIMM). Improved Kalman filters such as Unscented Kalman Filter (UKF), Iterated Extended Kalman Filter (IEKF), and Square-Root Cubature Kalman Filter (SRCKF) are designed to match vehicle dynamics models. The method’s performance is verified through in-vehicle semi-physical simulation experiments. Results show that the method significantly improves vehicle positioning accuracy in complex environments with varying satellite signal quality compared to traditional interactive multi-model algorithms.
Available online , doi: 10.11999/JEIT240427
Abstract:
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
Available online , doi: 10.11999/JEIT231366
Abstract:
To overcome the effect of channel estimation errors on the ineffectiveness of conventional optimal resource allocation algorithms, a robust resource allocation algorithm with imperfect Channel State Information(CSI) is proposed in Multiple-Input Single-Output(MISO) symbiotic radio systems. Considering the constraints of the minimum throughput of users, transmission time, maximum transmit power of the base station, and the reflection coefficients of users, based on bounded channel uncertainties, a robust throughput-maximization resource allocation problem is formulated by jointly optimizing transmission time, beamforming vectors, and reflection coefficients. The original problem is transformed into a convex problem by applying the Lagrange dual theory, the variable substitution, and the alternating optimizing methods. Simulation results verified that the throughput of the proposed algorithm is improved by 11.7% and the outage probability is reduced by 14.9% by comparing it with the non-robust resource allocation algorithm.
To overcome the effect of channel estimation errors on the ineffectiveness of conventional optimal resource allocation algorithms, a robust resource allocation algorithm with imperfect Channel State Information(CSI) is proposed in Multiple-Input Single-Output(MISO) symbiotic radio systems. Considering the constraints of the minimum throughput of users, transmission time, maximum transmit power of the base station, and the reflection coefficients of users, based on bounded channel uncertainties, a robust throughput-maximization resource allocation problem is formulated by jointly optimizing transmission time, beamforming vectors, and reflection coefficients. The original problem is transformed into a convex problem by applying the Lagrange dual theory, the variable substitution, and the alternating optimizing methods. Simulation results verified that the throughput of the proposed algorithm is improved by 11.7% and the outage probability is reduced by 14.9% by comparing it with the non-robust resource allocation algorithm.
Available online , doi: 10.11999/JEIT240398
Abstract:
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
Establishing accurate short-term forecasting models for electrical load is crucial for the stable operation and intelligent advancement of power systems. Traditional methods have not adequately addressed the issues of data volatility and model uncertainty. In this paper, a multi-dimensional adaptive short-term forecasting method for electrical load based on Bayesian Autoformer network is proposed. Specifically, an adaptive feature selection method is designed to capture multi-dimensional features. By capturing multi-scale features and time-frequency localized information, the model is enhanced to handle high volatility and nonlinear features in load data. Subsequently, an adaptive probabilistic forecasting model based on Bayesian Autoformer network is proposed. It captures relationships of significant subsequence features and associated uncertainties in load time series data, and dynamically updates the probability prediction model and parameter distributions through Bayesian optimization. The proposed model is subjected to a series of experimental analyses (comparative analysis, adaptive analysis, robustness analysis) on real load datasets of three different magnitudes (GW, MW, and KW). The model exhibits superior performance in adaptability and accuracy, with average improvements in Root Mean Square Error (RMSE), Pinball Loss, and Continuous Ranked Probability Score (CRPS) of 1.9%, 24.2%, and 4.5%, respectively.
Available online , doi: 10.11999/JEIT240428
Abstract:
In the era of big data, table widely exists in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
In the era of big data, table widely exists in various document images, and table detection is of great significance for the reuse of table information. In response to issues such as limited receptive field, reliance on predefined proposals, and inaccurate table boundary localization in existing table detection algorithms based on convolutional neural network, a table detection network based on DINO model is proposed in this paper. Firstly, an image preprocessing method is designed to enhance the corner and line features of table, enabling more precise table boundary localization and effective differentiation between table and other document elements like text. Secondly, a backbone network SwTNet-50 is designed, and Swin Transformer Blocks (STB) are introduced into ResNet to effectively combine local and global features, and the feature extraction ability of the model and the detection accuracy of table boundary are improved. Finally, to address the inadequacies in encoder feature learning in one-to-one matching and insufficient positive sample training in the DINO model, a collaborative hybrid assignments training strategy is adopted to improve the feature learning ability of the encoder and detection precision. Compared with various table detection methods based on deep learning, our model is better than other algorithms on the TNCR table detection dataset, with F1-Scores of 98.2%, 97.4%, and 93.3% for IoU thresholds of 0.5, 0.75, and 0.9, respectively. On the IIIT-AR-13K dataset, the F1-Score is 98.6% when the IoU threshold is 0.5.
Electromagnetic Sensitivity Analysis of Curved Boundaries under the Method of Accompanying Variables
Available online , doi: 10.11999/JEIT240432
Abstract:
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
Sensitivity analysis an evaluation method for the influence with variations of the design parameters on electromagnetic performance, which is utilized to calculate sensitivity information. This information guides the analysis of structural models to ensure compliance with design specifications. In the optimization design of electromagnetic structures by commercial software, traditional algorithms are often employed, involving adjustments to the geometry. However, this approach is known to be extensive in terms of computational time and resource consumption. In order to enhance the efficiency of model design, a stable and efficient processing scheme is proposed in the paper, known as the Adjoint Variable Method (AVM). This method achieves estimation of 1st～2nd order sensitivity on parameter transformations with only two algorithmic simulation conditions required. The application of AVM has predominantly been confined to the sensitivity analysis of rectangular boundary parameters, with this paper making the first extension of AVM to the sensitivity analysis of arc boundary parameters. Efficient analysis of the electromagnetic sensitivity of curved structures is accomplished based on the conditions designed for three distinct scenarios: fixed intrinsic parameters, frequency-dependent objective functions, and transient impulse functions. Compared to the Finite-Difference Method (FDM), a significant enhancement in computational efficiency is achieved by the proposed method. The effective implementation of the method substantially expands the application scope of AVM to curved boundaries, which can be utilized in optimization problems such as the electromagnetic structures of plasma models and the edge structures of complex antenna models. When computational resources are limited, the reliability and stability of electromagnetic structure optimization can be ensured by the application of the proposed method.
Available online , doi: 10.11999/JEIT240411
Abstract:
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms other benchmark algorithms.
It can effectively overcome the limitations of the ground environment, expand the network coverage and provide users with convenient computing services, through constructing the air-ground integrated edge computing network with Unmanned Aerial Vehicle (UAV) as the relay. In this paper, with the objective of maximizing the task completion amount, the joint optimization problem of UAV deployment, user-server association and bandwidth allocation is investigated in the context of the UAV assisted multi-user and multi-server edge computing network. The formulated joint optimization problem contains both continuous and discrete variables, which makes itself hard to solve. To this end, a Block Coordinated Descent (BCD) based iterative algorithm is proposed in this paper, involving the optimization tools such as differential evolution and particle swarm optimization. The original problem is decomposed into three sub-problems with the proposed algorithm, which can be solved independently. The optimal solution of the original problem can be approached through the iteration among these three subproblems. Simulation results show that the proposed algorithm can greatly increase the amount of completed tasks, which outperforms other benchmark algorithms.
Available online , doi: 10.11999/JEIT231278
Abstract:
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the\begin{document}$ {\ell _p} $\end{document}
In active electrical scanning millimeter-wave security imaging, the uniform array antenna has the bottleneck of uncontrolled cost and high complexity, which is difficult to be widely applied in practices. To this end, a near-field focused sparse array design algorithm for high sparsity and low sidelobes is proposed in this paper. It applies an improved three dimensional (3D) time-domain imaging algorithm to achieve high-accuracy 3D reconstruction. Firstly, the near-field focusing sparse array antenna model is constructed by taking the near-field focusing position and peak sidelobe level as constraints, where the
Available online , doi: 10.11999/JEIT240370
Abstract:
To address the pulse dispersion issue in detecting frequency-shifted chirp signals with traditional Fractional Fourier Transform (FrFT), an adaptive FrFT detection method is proposed in this paper. Leveraging the structural model of short packets and the Neyman-Pearson detection model, an analytical method is derived to evaluate the false alarm probability and missed detection probability of signal frame detection using an evaluation function and a decision threshold. Incorporating the pulse characteristics of traditional FrFT for complete chirp signals, a correction scheme for the fractional Fourier integral operator is proposed, and the peak distribution function of the frequency-shifted chirp symbol is derived for the adaptive FrFT. Addressing the search time shift issue in the adaptive FrFT detection process, the peak size and distribution of the frequency-shifted chirp symbol are analyzed, and the superiority of the adaptive FrFT detection compared to traditional FrFT is demonstrated.
To address the pulse dispersion issue in detecting frequency-shifted chirp signals with traditional Fractional Fourier Transform (FrFT), an adaptive FrFT detection method is proposed in this paper. Leveraging the structural model of short packets and the Neyman-Pearson detection model, an analytical method is derived to evaluate the false alarm probability and missed detection probability of signal frame detection using an evaluation function and a decision threshold. Incorporating the pulse characteristics of traditional FrFT for complete chirp signals, a correction scheme for the fractional Fourier integral operator is proposed, and the peak distribution function of the frequency-shifted chirp symbol is derived for the adaptive FrFT. Addressing the search time shift issue in the adaptive FrFT detection process, the peak size and distribution of the frequency-shifted chirp symbol are analyzed, and the superiority of the adaptive FrFT detection compared to traditional FrFT is demonstrated.
Available online , doi: 10.11999/JEIT240417
Abstract:
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8 k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8 k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
Available online , doi: 10.11999/JEIT240241
Abstract:
The 6th Generation (6G) mobile communication network is required to provide Ultra-Reliable and Low-Latency Communication(URLLC) services for large-scale nodes. Considering the multi-user massive Multiple-Input Multiple-Out(MIMO) technology-assisted URLLC downlink communication scenario, system performance is characterized based on the Finite BlockLength(FBL) regime theory, and an efficient power allocation algorithm is proposed to improve the users’ transmission rate under fairness issue. Specifically, the traditional MIMO systems utilize the global Singular Value Decomposition(SVD) linear precoding scheme, leading to high complexity and inability to guarantee the fairness of rates among users. To deal with these challenges, a precoding scheme based on the local SVD is proposed to effectively suppress inter-user interference and intra-user interference of MIMO system with relatively low complexity. Secondly, the optimization problem is formulated, where the power allocation factors are optimized to Maximize Minimum Rate (MMR) among users. In order to efficiently solve the non-convex problem containing high-dimensional variables which are coupled with each other, the Shannon capacity term in the objective function is relaxed by introducing auxiliary variables and piecewise McCormick envelopes, and it is transformed into convex functions, thereby reformulating the MMR problem. An optimization algorithm based on the Successive Convex Approximation (SCA) is proposed to solve the reformulated problem effectively. Simulation results validate the convergence and accuracy of the proposed optimization algorithm, and it is shown that the proposed optimization algorithm has advantages over the existing schemes in terms of system MMR performance and robustness.
The 6th Generation (6G) mobile communication network is required to provide Ultra-Reliable and Low-Latency Communication(URLLC) services for large-scale nodes. Considering the multi-user massive Multiple-Input Multiple-Out(MIMO) technology-assisted URLLC downlink communication scenario, system performance is characterized based on the Finite BlockLength(FBL) regime theory, and an efficient power allocation algorithm is proposed to improve the users’ transmission rate under fairness issue. Specifically, the traditional MIMO systems utilize the global Singular Value Decomposition(SVD) linear precoding scheme, leading to high complexity and inability to guarantee the fairness of rates among users. To deal with these challenges, a precoding scheme based on the local SVD is proposed to effectively suppress inter-user interference and intra-user interference of MIMO system with relatively low complexity. Secondly, the optimization problem is formulated, where the power allocation factors are optimized to Maximize Minimum Rate (MMR) among users. In order to efficiently solve the non-convex problem containing high-dimensional variables which are coupled with each other, the Shannon capacity term in the objective function is relaxed by introducing auxiliary variables and piecewise McCormick envelopes, and it is transformed into convex functions, thereby reformulating the MMR problem. An optimization algorithm based on the Successive Convex Approximation (SCA) is proposed to solve the reformulated problem effectively. Simulation results validate the convergence and accuracy of the proposed optimization algorithm, and it is shown that the proposed optimization algorithm has advantages over the existing schemes in terms of system MMR performance and robustness.
Available online , doi: 10.11999/JEIT240348
Abstract:
The coprime array has garnered noticeable attention due to the reduced mutual coupling, but the alternately deployed subarrays limit the enhancement of consecutive degrees of freedom to some extent. To address this deficiency, two types of Coprime Array with Translated Subarray (CATrS) are presented based on the analysis of the conditions for generating redundant virtual arrays in the cross-difference sets. Firstly, the subarray is shifted to an appropriate location to optimize the array geometry, and then the displacement distance is analyzed. Subsequently, the closed-form expressions for the degrees of freedom, consecutive degrees of freedom, hole positions, and weight functions of CATrS are derived. Theoretical analysis indicates that CATrS can effectively increase both the number of degrees of freedom and consecutive degrees of freedom, while suppress the mutual coupling without altering the number of physical sensors. Finally, the superiorities of CATrS in direction of arrival estimation are verified through simulation experiments.
The coprime array has garnered noticeable attention due to the reduced mutual coupling, but the alternately deployed subarrays limit the enhancement of consecutive degrees of freedom to some extent. To address this deficiency, two types of Coprime Array with Translated Subarray (CATrS) are presented based on the analysis of the conditions for generating redundant virtual arrays in the cross-difference sets. Firstly, the subarray is shifted to an appropriate location to optimize the array geometry, and then the displacement distance is analyzed. Subsequently, the closed-form expressions for the degrees of freedom, consecutive degrees of freedom, hole positions, and weight functions of CATrS are derived. Theoretical analysis indicates that CATrS can effectively increase both the number of degrees of freedom and consecutive degrees of freedom, while suppress the mutual coupling without altering the number of physical sensors. Finally, the superiorities of CATrS in direction of arrival estimation are verified through simulation experiments.
Available online , doi: 10.11999/JEIT240395
Abstract:
Ambient Backscatter Communication (AmBC) is a new low-power communication technology, which can use Radio Frequency (RF) signal sources in the surrounding environment to realize passive information transmission, but because of the problems such as double fading and obstacles, the signal strength of the reflection link is weak. Therefore, the Intelligent Reflecting Surface (IRS) is introduced into the AmBC system to enhance the reflection link gain. However, both IRS and tag are passive devices, making channel estimation extremely challenging. To solve this problem, an IRS-assisted channel estimation scheme for AmBC system is proposed. First, the channel is decomposed into multiple subchannels, where each subchannel of the reflection link corresponds to an IRS reflection unit. Then, the joint design of IRS reflection patterns and channel estimation is explored by using the least squares method as an estimation criterion with the objective of minimizing the Mean Square Error (MSE). Simulation results show that the channel estimation scheme has good estimation performance.
Ambient Backscatter Communication (AmBC) is a new low-power communication technology, which can use Radio Frequency (RF) signal sources in the surrounding environment to realize passive information transmission, but because of the problems such as double fading and obstacles, the signal strength of the reflection link is weak. Therefore, the Intelligent Reflecting Surface (IRS) is introduced into the AmBC system to enhance the reflection link gain. However, both IRS and tag are passive devices, making channel estimation extremely challenging. To solve this problem, an IRS-assisted channel estimation scheme for AmBC system is proposed. First, the channel is decomposed into multiple subchannels, where each subchannel of the reflection link corresponds to an IRS reflection unit. Then, the joint design of IRS reflection patterns and channel estimation is explored by using the least squares method as an estimation criterion with the objective of minimizing the Mean Square Error (MSE). Simulation results show that the channel estimation scheme has good estimation performance.
Available online , doi: 10.11999/JEIT240666
Abstract:
In monitoring Internet of Things (IoT), the sensor devices need to transmit collected information back to the Access Point (AP) in a timely manner under energy-limited conditions, both the Age of Information (AoI) and Energy Efficiency (EE) are important to the systems. The AoI and EE tradeoff for multi-device monitoring systems is investigated in this paper, where the sensor devices transmit monitoring information to the AP via short packets with the assistance of an Intelligent Reflective Surface (IRS). To avoid packet collisions caused by multiple devices occupying the same resource block, an access control protocol is proposed, and the closed-form expressions for average AoI and EE are derived. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off AoI and EE by optimizing the transmission power. Simulation results verify the correctness of our theoretical analysis and demonstrate that the proposed scheme can achieve better AoI and EE performances. Moreover, the proposed algorithm is able to find the optimal AoI and EE tradeoff point.
In monitoring Internet of Things (IoT), the sensor devices need to transmit collected information back to the Access Point (AP) in a timely manner under energy-limited conditions, both the Age of Information (AoI) and Energy Efficiency (EE) are important to the systems. The AoI and EE tradeoff for multi-device monitoring systems is investigated in this paper, where the sensor devices transmit monitoring information to the AP via short packets with the assistance of an Intelligent Reflective Surface (IRS). To avoid packet collisions caused by multiple devices occupying the same resource block, an access control protocol is proposed, and the closed-form expressions for average AoI and EE are derived. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off AoI and EE by optimizing the transmission power. Simulation results verify the correctness of our theoretical analysis and demonstrate that the proposed scheme can achieve better AoI and EE performances. Moreover, the proposed algorithm is able to find the optimal AoI and EE tradeoff point.
Available online , doi: 10.11999/JEIT240645
Abstract:
To solve the degradation problems such as color distortion, low brightness, and detail loss in images under transformer oil, a multi-scale weighted Retinex algorithm for image enhancement is proposed in this paper. Firstly, in order to alleviate the color distortion problem of image under transformer oil, a hybrid dynamic color channel compensation algorithm is proposed, which dynamically compensates according to the attenuation state of each channel of the captured image. Then, in order to solve the problem of detail loss, a sharpening weight strategy is proposed. Finally, pyramid multi-scale fusion strategy is used to weighted fuse different-scale Retinex reflection components and corresponding weight maps to obtain clear images under transformer oil. Experimental results demonstrate that the algorithm proposed in this paper can effectively solve the complex degradation problem of image under transformer oil.
To solve the degradation problems such as color distortion, low brightness, and detail loss in images under transformer oil, a multi-scale weighted Retinex algorithm for image enhancement is proposed in this paper. Firstly, in order to alleviate the color distortion problem of image under transformer oil, a hybrid dynamic color channel compensation algorithm is proposed, which dynamically compensates according to the attenuation state of each channel of the captured image. Then, in order to solve the problem of detail loss, a sharpening weight strategy is proposed. Finally, pyramid multi-scale fusion strategy is used to weighted fuse different-scale Retinex reflection components and corresponding weight maps to obtain clear images under transformer oil. Experimental results demonstrate that the algorithm proposed in this paper can effectively solve the complex degradation problem of image under transformer oil.
Available online , doi: 10.11999/JEIT240388
Abstract:
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction result, a frequency separation generative adversarial super-resolution reconstruction network based on dense residuals and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction result, a frequency separation generative adversarial super-resolution reconstruction network based on dense residuals and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
Available online , doi: 10.11999/JEIT240521
Abstract:
Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
Under the non-complete and highly dynamic active jamming confrontation combat environment, the static model optimized and trained for multiple types of single active jamming samples in the library at this stage is unable to update the model quickly and difficult to cope with the problem of imbalance of test samples in the face of compound jamming outside the library of diverse types, variable parameters and multiple combinations. Considering this problem, a zero-memory incremental learning-based radar compound active jamming recognition method is proposed in this paper. Firstly, a meta-learning training model is utilized to learn a prototype of a single jamming within the library, and an efficient feature extractor is trained so that it has the ability to effectively extract the features of the compound jamming outside the library. Further, based on the hyperdimensional space and cosine similarity calculation, a Zero-Memory Incremental Learning Network (ZMILN) is constructed to map the compound jamming prototype vectors into the hyperdimensional space and store them, so as to realize the dynamic update of the recognition model. In addition, in order to solve the compound jamming recognition problem under sample imbalance, the Transductive Information Maximization (TIM) test module is designed to further strengthen the training of the recognition model to cope with the imbalanced test samples by introducing scatter constraints in the mutual information loss function. Experimental results show that the method proposed in this paper achieves an average recognition accuracy of 93.62% after incremental learning for four types of single jamming and seven types of compound jamming under imbalance test conditions. The method realizes fast and dynamic recognition of compound jamming outside the library under multiple combination conditions by fully extracting the knowledge of multiple types of single jamming in the library.
Available online , doi: 10.11999/JEIT240236
Abstract:
As a new technology to reconfigure wireless communication environment by intelligently controlling signal reflection via algorithms, Intelligent Reflecting Surface (IRS) has attracted lots of attention in recent years. Compared with the conventional relay system, the relay system aided by IRS can effectively save the cost and energy consumption, and significantly enhance the system performance. However, the phase quantization error generated by IRS with discrete phase shifter may degrade the performance of the receiver. To analyze the performance loss arising from IRS phase quantization error, in accordance with the weak law of large numbers and Rayleigh distribution, the closed-form expressions for the Signal-to-Noise Ratio (SNR) performance loss and achievable rate of the double IRS-aided amplify-and-forward relay network, which are associated with the number of phase shifter quantization bits, are derived in the Rayleigh channels. In addition, their approximate performance loss closed-form expressions are also derived based on the Taylor series expansion. Simulation results show that the performance losses of SNR and achievable rate decrease gradually with the number of quantization bits, and increase gradually with the number of IRS phase shift elements. When the number of IRS phase shift elements is 4, the performance losses of SNR and reachable rate are less than 0.06 dB and 0.03 bits/(s·Hz), respectively.
As a new technology to reconfigure wireless communication environment by intelligently controlling signal reflection via algorithms, Intelligent Reflecting Surface (IRS) has attracted lots of attention in recent years. Compared with the conventional relay system, the relay system aided by IRS can effectively save the cost and energy consumption, and significantly enhance the system performance. However, the phase quantization error generated by IRS with discrete phase shifter may degrade the performance of the receiver. To analyze the performance loss arising from IRS phase quantization error, in accordance with the weak law of large numbers and Rayleigh distribution, the closed-form expressions for the Signal-to-Noise Ratio (SNR) performance loss and achievable rate of the double IRS-aided amplify-and-forward relay network, which are associated with the number of phase shifter quantization bits, are derived in the Rayleigh channels. In addition, their approximate performance loss closed-form expressions are also derived based on the Taylor series expansion. Simulation results show that the performance losses of SNR and achievable rate decrease gradually with the number of quantization bits, and increase gradually with the number of IRS phase shift elements. When the number of IRS phase shift elements is 4, the performance losses of SNR and reachable rate are less than 0.06 dB and 0.03 bits/(s·Hz), respectively.
Available online , doi: 10.11999/JEIT240446
Abstract:
In the future, 6G will realize a new era of intelligent interconnection of everything and the combina-tion of virtual and reality, which can not be separated from the development of communication and perception technology. However, due to the scarcity of frequency resources, the sharing of frequency resources between them is an urgent problem to be solved. Integrated Sensing and Communication (ISAC) technology provides a new way to solve this problem. It allows communication and perception to share a set of equipment and frequency resources, and can simultaneously complete target detection and information communication, which is considered to be one of the key technologies of 6G. At the same time, China is powerful in the ocean with abundant marine resources and the demands for marine communication and marine targets perception have increased greatly. In this paper, ISAC technology in marine environment was studied, and a weighted waveform optimization design method is proposed. The simulation results show that when the power ratio of communication to perception is within the range of [0.2, 0.5], the integrated waveform not only has good communication performance, but also has good perception performance. Finally, the future work is prospected.
In the future, 6G will realize a new era of intelligent interconnection of everything and the combina-tion of virtual and reality, which can not be separated from the development of communication and perception technology. However, due to the scarcity of frequency resources, the sharing of frequency resources between them is an urgent problem to be solved. Integrated Sensing and Communication (ISAC) technology provides a new way to solve this problem. It allows communication and perception to share a set of equipment and frequency resources, and can simultaneously complete target detection and information communication, which is considered to be one of the key technologies of 6G. At the same time, China is powerful in the ocean with abundant marine resources and the demands for marine communication and marine targets perception have increased greatly. In this paper, ISAC technology in marine environment was studied, and a weighted waveform optimization design method is proposed. The simulation results show that when the power ratio of communication to perception is within the range of [0.2, 0.5], the integrated waveform not only has good communication performance, but also has good perception performance. Finally, the future work is prospected.
Available online , doi: 10.11999/JEIT240377
Abstract:
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
The Flying Ad-hoc NETworks (FANETs) are widely used in emergency rescue scenarios due to their high mobility and self-organization advantages. In emergency scenarios, a large number of user paging requests lead to a challenging coordination between the surge in local traffic and the limited spectrum resources, significant channel interference issues in FANETs are resulted from. There is an urgent need to extend the high spectrum utilization advantage of Partially Overlapping Channels (POCs) to emergency scenarios. However, the adjacent channel characteristics of POCs leads to complex interference that is difficult to characterize. Therefore, partial overlapping channel allocation methods in FANETs are studied in this paper. By utilizing geometric prediction to reconstruct time-varying interference graphs and characterizing the POCs interference model with the interference-free minimum channel spacing matrix, a Dynamic Channel Allocation algorithm for POCs based on Upper Confidence Bounds (UCB-DCA) is proposed. This algorithm aims to solve for an approximately optimal channel allocation scheme through distributed decision-making. Simulation results demonstrate that the algorithm achieves a trade-off between network interference and channel switching times, and has good convergence performance.
Available online , doi: 10.11999/JEIT240302
Abstract:
As the scale of Unmanned Aerial Vehicle (UAV) systems and the demand for higher communication rates continue to grow, UAV Optical Mobile Communications (UAV-OMC) has emerged as a promising technical direction. However, it is difficult for traditional UAV-OMC to support multiple UAVs’ communications. In this paper, based on the Optical Intelligent Reflecting Surface (OIRS) technology, we propose a distributed OMC system for UAV clusters. By setting the OIRS on a specific UAV, we utilize OIRS to spread the optical signal from a single UAV node to multiple UAV nodes. While retaining the high energy efficiency and high speed of the UAV-OMC system, this system can support the communication of distributed UAV clusters. This paper conducts mathematical modeling of the proposed system. When modeling the system, we took into account a series of realistic factors, such as OIRS beam control, relative motion between UAVs, UAV jitter, which fit the actual system. Closed-form expressions for the system's Bit Error Rate (BER) and asymptotic outage probability are also derived. Based on theoretical analysis and simulation results, the effect of each parameter and system design have been discussed.
As the scale of Unmanned Aerial Vehicle (UAV) systems and the demand for higher communication rates continue to grow, UAV Optical Mobile Communications (UAV-OMC) has emerged as a promising technical direction. However, it is difficult for traditional UAV-OMC to support multiple UAVs’ communications. In this paper, based on the Optical Intelligent Reflecting Surface (OIRS) technology, we propose a distributed OMC system for UAV clusters. By setting the OIRS on a specific UAV, we utilize OIRS to spread the optical signal from a single UAV node to multiple UAV nodes. While retaining the high energy efficiency and high speed of the UAV-OMC system, this system can support the communication of distributed UAV clusters. This paper conducts mathematical modeling of the proposed system. When modeling the system, we took into account a series of realistic factors, such as OIRS beam control, relative motion between UAVs, UAV jitter, which fit the actual system. Closed-form expressions for the system's Bit Error Rate (BER) and asymptotic outage probability are also derived. Based on theoretical analysis and simulation results, the effect of each parameter and system design have been discussed.
Available online , doi: 10.11999/JEIT240518
Abstract:
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
Reconfigurable Intelligent Surfaces (RIS) is considered as one of the potential key technologies for 6G mobile communications, which offers advantages such as low cost, low energy consumption, and easy deployment. By integrating RIS technology into marine wireless channels, it has the capability to convert the unpredictable wireless transmission environment into a manageable one. However, current channel models are struggling to accurately depict the unique signal transmission mechanisms of RIS-enabled base station to ship channels in marine communication scenarios, resulting in challenges in achieving a balance between accuracy and complexity for channel characterization and theoretical establishment. Therefore, this paper develops a segmented channel modeling method for near-field RIS-enabled marine communications, and then proposed a multi-domain joint parameterized statistical channel model for RIS-enabled marine communications. This approach focus on addressing the technical bottleneck of existing RIS channel modeling methods that face difficulties in achieving a balance between accuracy and efficiency, ultimately facilitating the rapid development of the 6G mobile communication industry in China.
Available online , doi: 10.11999/JEIT240003
Abstract:
As a new information communication technology based on software and hardware resource sharing and information sharing, Integration of Sensing and Communication (ISAC) can integrate wireless sensing into Wi-Fi platforms, providing an efficient method for low-cost indoor localization. Focusing on the problem of real-time and accuracy of indoor positioning parameter estimation, a joint parameter estimation algorithm based on three Dimensional (3D) Matrix Pencil (MP) is proposed. First, the Channel State Information (CSI) data is analyzed and a 3D matrix containing Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) is constructed. Secondly, the 3D matrix is smoothed and the 3D MP algorithm is used for parameter estimation, the direct path is found by clustering. Finally, the triangulation method is used for positioning to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the MUltiple SIgnal Classification (MUSIC) parameter estimation algorithm, there is no need for complicated peak search steps, and the computational complexity is reduced by 90%. Compared with the two-dimensional MP algorithm, adding DFS can effectively improve the resolution and accuracy of parameter estimation. The actual test verifies that the proposed algorithm can achieve an average positioning accuracy of 0.56 m at a confidence level of 67% indoors. Therefore, the proposed algorithm effectively improves the real-time and accuracy of the existing indoor positioning parameter estimation.
As a new information communication technology based on software and hardware resource sharing and information sharing, Integration of Sensing and Communication (ISAC) can integrate wireless sensing into Wi-Fi platforms, providing an efficient method for low-cost indoor localization. Focusing on the problem of real-time and accuracy of indoor positioning parameter estimation, a joint parameter estimation algorithm based on three Dimensional (3D) Matrix Pencil (MP) is proposed. First, the Channel State Information (CSI) data is analyzed and a 3D matrix containing Angle of Arrival (AoA), Time of Flight (ToF), and Doppler Frequency Shift (DFS) is constructed. Secondly, the 3D matrix is smoothed and the 3D MP algorithm is used for parameter estimation, the direct path is found by clustering. Finally, the triangulation method is used for positioning to verify the effectiveness of the proposed algorithm. Experimental results show that compared with the MUltiple SIgnal Classification (MUSIC) parameter estimation algorithm, there is no need for complicated peak search steps, and the computational complexity is reduced by 90%. Compared with the two-dimensional MP algorithm, adding DFS can effectively improve the resolution and accuracy of parameter estimation. The actual test verifies that the proposed algorithm can achieve an average positioning accuracy of 0.56 m at a confidence level of 67% indoors. Therefore, the proposed algorithm effectively improves the real-time and accuracy of the existing indoor positioning parameter estimation.
Available online , doi: 10.11999/JEIT240503
Abstract:
A novel approach is proposed to integrate the Detection and Avoidance Alerting Logic for Unmanned Aircraft Systems (DAIDALUS) with the Markov Decision Process (MDP) to address the decision-making challenges associated with collision avoidance in Unmanned Aerial Vehicle (UAV) Detection and Avoidance (DAA) systems. The guidance logic inherent in the DAIDALUS algorithm is utilized to compute drone collision avoidance strategies based on the current state space. These strategies are subsequently employed as the action space for the MDP, with suitable reward functions and state transition probabilities defined to establish an MDP model. The model is then used to investigate the effects of various discount factors on the UAV flight collision avoidance process. The simulation results show that compared to DAIDALUS, the efficiency of this method has increased by 27.2%;when the discount factor is set to 0.99, it can balance long-term and short-term returns; The net intrusion rate is 5.8%, and the closest distance between the threatening aircraft and the local aircraft is 343 meters, which can meet the requirements of collision avoidance during drone flight.
A novel approach is proposed to integrate the Detection and Avoidance Alerting Logic for Unmanned Aircraft Systems (DAIDALUS) with the Markov Decision Process (MDP) to address the decision-making challenges associated with collision avoidance in Unmanned Aerial Vehicle (UAV) Detection and Avoidance (DAA) systems. The guidance logic inherent in the DAIDALUS algorithm is utilized to compute drone collision avoidance strategies based on the current state space. These strategies are subsequently employed as the action space for the MDP, with suitable reward functions and state transition probabilities defined to establish an MDP model. The model is then used to investigate the effects of various discount factors on the UAV flight collision avoidance process. The simulation results show that compared to DAIDALUS, the efficiency of this method has increased by 27.2%;when the discount factor is set to 0.99, it can balance long-term and short-term returns; The net intrusion rate is 5.8%, and the closest distance between the threatening aircraft and the local aircraft is 343 meters, which can meet the requirements of collision avoidance during drone flight.
Available online , doi: 10.11999/JEIT240612
Abstract:
In recent years, Active Reconfigurable Intelligent Surface (ARIS) technology has received extensive attention from academia. However, there is still a lack of research on the application of ARIS in multi-RIS assisted wireless communication systems. A dual RIS assisted wireless communication system is proposed in this paper The model assumes that the direct link between the Base Station (BS) and the user is blocked and only communicates through the reflection link formed by the RIS. On this basis, according to the different combinations of ARIS and Passive RIS (PRIS), four RIS combination models are proposed. The model aims to maximize the communication capacity of system by optimizing the BS beamforming, RIS phase matrix and power allocation factor. To tackle this non-convex optimization problem, the paper proposes an Alternating Optimization (AO) algorithm and employs Successive Convex Approximation (SCA). Simulation results illustrated that under the condition of total power constraints, the performance of the Transmitter-ARIS ARIS-Receive combination model significantly superior to the traditional signal ARIS model, regardless of whether the transmit power of the BS is high or low.
In recent years, Active Reconfigurable Intelligent Surface (ARIS) technology has received extensive attention from academia. However, there is still a lack of research on the application of ARIS in multi-RIS assisted wireless communication systems. A dual RIS assisted wireless communication system is proposed in this paper The model assumes that the direct link between the Base Station (BS) and the user is blocked and only communicates through the reflection link formed by the RIS. On this basis, according to the different combinations of ARIS and Passive RIS (PRIS), four RIS combination models are proposed. The model aims to maximize the communication capacity of system by optimizing the BS beamforming, RIS phase matrix and power allocation factor. To tackle this non-convex optimization problem, the paper proposes an Alternating Optimization (AO) algorithm and employs Successive Convex Approximation (SCA). Simulation results illustrated that under the condition of total power constraints, the performance of the Transmitter-ARIS ARIS-Receive combination model significantly superior to the traditional signal ARIS model, regardless of whether the transmit power of the BS is high or low.
Available online , doi: 10.11999/JEIT240018
Abstract:
Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) is able to create an all-space intelligent radio environment to effectively improve the performance of wireless communication systems, thus it has vast research potential. Therefore, in this paper, a large-scale STAR-RIS-assisted near-field Integrated Sensing and Communication (ISAC) approach is proposed. Cramér-Rao Bound (CRB) of the three-dimensional estimation of the sensing target is optimized. First, the near-field system model is built and then beam steering vectors between base station, STAR-RIS, communication users, sensing target and sensor are derived respectively. Second, the sensing performance is optimized by designing the transmit beamforming matrix, the covariance matrix of transmit signal and the STAR-RIS coefficients. Third, a non-convex optimization problem is solved via semi-definite relaxation approach. The simulation results show the effectiveness of our proposed ISAC approach, and the positioning performance advantage brought by the extra distance freedom of near field.
Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) is able to create an all-space intelligent radio environment to effectively improve the performance of wireless communication systems, thus it has vast research potential. Therefore, in this paper, a large-scale STAR-RIS-assisted near-field Integrated Sensing and Communication (ISAC) approach is proposed. Cramér-Rao Bound (CRB) of the three-dimensional estimation of the sensing target is optimized. First, the near-field system model is built and then beam steering vectors between base station, STAR-RIS, communication users, sensing target and sensor are derived respectively. Second, the sensing performance is optimized by designing the transmit beamforming matrix, the covariance matrix of transmit signal and the STAR-RIS coefficients. Third, a non-convex optimization problem is solved via semi-definite relaxation approach. The simulation results show the effectiveness of our proposed ISAC approach, and the positioning performance advantage brought by the extra distance freedom of near field.
Available online , doi: 10.11999/JEIT240561
Abstract:
Considering the limitations of traditional joint beamforming methods in optimizing Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication systems, such as solely focusing on the phase shift matrix optimization of RIS and the lack of universality in the optimization approach, a joint beamforming method based on Cooperative Co-Evolutionary Algorithm (CCEA) for the RIS-assisted UAV multi-user communication system is proposed. This method decomposes the joint beamforming problem into subproblems involving RIS reflection beam design and transmitter beam design, which are solved through information exchange and collaboration during the independent evolutionary process of two subpopulations. Simulation results demonstrate that compared to joint beamforming optimization only considering RIS phase shift matrix design, CCEA changes the energy distribution of the reflection wave in three-dimensional space by optimizing the RIS reflection wave shape, leading to improved reception-side Signal-to-Interference-plus-Noise Ratio (SINR) and spectral efficiency. Additionally, CCEA generates more diverse solutions that effectively cover user directions at various UAV and user positions, avoiding local optima and exhibiting greater applicability across different scenarios compared to traditional methods.
Considering the limitations of traditional joint beamforming methods in optimizing Reconfigurable Intelligent Surface (RIS)-assisted Unmanned Aerial Vehicle (UAV) communication systems, such as solely focusing on the phase shift matrix optimization of RIS and the lack of universality in the optimization approach, a joint beamforming method based on Cooperative Co-Evolutionary Algorithm (CCEA) for the RIS-assisted UAV multi-user communication system is proposed. This method decomposes the joint beamforming problem into subproblems involving RIS reflection beam design and transmitter beam design, which are solved through information exchange and collaboration during the independent evolutionary process of two subpopulations. Simulation results demonstrate that compared to joint beamforming optimization only considering RIS phase shift matrix design, CCEA changes the energy distribution of the reflection wave in three-dimensional space by optimizing the RIS reflection wave shape, leading to improved reception-side Signal-to-Interference-plus-Noise Ratio (SINR) and spectral efficiency. Additionally, CCEA generates more diverse solutions that effectively cover user directions at various UAV and user positions, avoiding local optima and exhibiting greater applicability across different scenarios compared to traditional methods.
Available online , doi: 10.11999/JEIT240092
Abstract:
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
Synthetic Aperture Radar (SAR) is a microwave remote sensing imaging radar. In recent years, with the advancement of digital technology and radio frequency electronic technology, the jamming technology of SAR imaging is developed rapidly. The active jamming such as deception jamming based on Digital Radio Frequency Memory (DRFM) technology brings serious challenges to SAR imaging systems for civil use and military use. For research on SAR anti-jamming imaging against deception jamming, firstly, orthogonal waveform diversity design and waveform optimization is carried out for Orthogonal Frequency Division Multiplexing waveforms with Cyclic Prefixes (CP-OFDM). And the CP-OFDM wide band orthogonal waveform set with excellent autocorrelation peak sidelobe level and cross-correlation peak level is obtained. Then the sparse SAR imaging theory is introduced, which is combined with CP-OFDM. By using the sparse reconstruction method, the high-quality and high-precision imaging with anti-jamming capability is realized. Finally, simulation based on point targets, surface targets and real data is conducted, and it is proved that the method can completely remove the false targets generated by deception jamming, suppress sidelobes and achieve high-precision imaging.
Available online , doi: 10.11999/JEIT240488
Abstract:
When the locations of the Base Station (BS) and user are fixed and the sum of the distances from BS to the Intelligent Reflecting Surface (IRS) and from the IRS to the user is given, the optimal placement of passive and active IRSs based on the maximizing achievable rate criterion under line-of-sight and Rayleigh channels are analyzed in this paper. First, the phase alignment and the law of large numbers are employed to derive the close-form expressions of the achievable rates of passive and active IRS-assisted wireless networks. Then, the effects of the path loss exponent\begin{document}${\beta _1}$\end{document} \begin{document}${\beta _2}$\end{document} \begin{document}${\beta _1} \gt {\beta _2}$\end{document} \begin{document}${\beta _1}$\end{document} \begin{document}${\beta _2}$\end{document} \begin{document}${\beta _1} < {\beta _2}$\end{document} \begin{document}${\beta _1} = {\beta _2}$\end{document}
When the locations of the Base Station (BS) and user are fixed and the sum of the distances from BS to the Intelligent Reflecting Surface (IRS) and from the IRS to the user is given, the optimal placement of passive and active IRSs based on the maximizing achievable rate criterion under line-of-sight and Rayleigh channels are analyzed in this paper. First, the phase alignment and the law of large numbers are employed to derive the close-form expressions of the achievable rates of passive and active IRS-assisted wireless networks. Then, the effects of the path loss exponent
Available online , doi: 10.11999/JEIT240431
Abstract:
Salient Object Detection (SOD) aims to mimic the human visual system’s attention and cognitive mechanisms to automatically extract prominent objects from a scene. While existing Convolutional Neural Network (CNN)- or Transformer-based models continuously advance performance in this field, there is less research addressing two specific issues: (1) Most methods in this domain commonly use a pixel-wise dense prediction approach to obtain pixel saliency values. However, this approach does not align with the human visual system’s scene analysis mechanism, where the human eye usually performs a holistic analysis of semantic regions rather than focusing on pixel-level information. (2) Enhancing contextual information association is widely emphasized in SOD tasks, but acquiring long-range contextual features through a Transformer backbone does not necessarily offer advantages. SOD should focus more on the center-neighborhood differences within appropriate regions rather than global long-range dependencies. To address these issues, we propose a novel salient object detection model that integrates CNN-based adaptive attention and masked attention into the network to enhance SOD performance. The proposed algorithm designs a mask-aware decoding module that perceives image features by restricting cross-attention to the predicted mask region, helping the network better focus on the entire region of salient objects. Additionally, we design a convolutional attention-based contextual feature enhancement module, which, unlike Transformers that establish long-range relationships layer by layer, captures appropriate contextual associations only in the highest-level features, avoiding the introduction of irrelevant global information. We conducted experimental evaluations on four widely used datasets, and the results demonstrate that our proposed method achieves significant performance improvements across different scenarios, showcasing good generalization ability and stability.
Salient Object Detection (SOD) aims to mimic the human visual system’s attention and cognitive mechanisms to automatically extract prominent objects from a scene. While existing Convolutional Neural Network (CNN)- or Transformer-based models continuously advance performance in this field, there is less research addressing two specific issues: (1) Most methods in this domain commonly use a pixel-wise dense prediction approach to obtain pixel saliency values. However, this approach does not align with the human visual system’s scene analysis mechanism, where the human eye usually performs a holistic analysis of semantic regions rather than focusing on pixel-level information. (2) Enhancing contextual information association is widely emphasized in SOD tasks, but acquiring long-range contextual features through a Transformer backbone does not necessarily offer advantages. SOD should focus more on the center-neighborhood differences within appropriate regions rather than global long-range dependencies. To address these issues, we propose a novel salient object detection model that integrates CNN-based adaptive attention and masked attention into the network to enhance SOD performance. The proposed algorithm designs a mask-aware decoding module that perceives image features by restricting cross-attention to the predicted mask region, helping the network better focus on the entire region of salient objects. Additionally, we design a convolutional attention-based contextual feature enhancement module, which, unlike Transformers that establish long-range relationships layer by layer, captures appropriate contextual associations only in the highest-level features, avoiding the introduction of irrelevant global information. We conducted experimental evaluations on four widely used datasets, and the results demonstrate that our proposed method achieves significant performance improvements across different scenarios, showcasing good generalization ability and stability.
Available online , doi: 10.11999/JEIT240524
Abstract:
The Draco algorithm is an example of a stream cipher based on the Consisting of the Initial Value and Key-prefix (CIVK) scheme, claiming to have provable security against Time Memory Data TradeOff (TMDTO) attacks. However, its selection function has structural flaws, which have been exploited attackers to provide analyses that break its security boundaries. Addressing the security vulnerabilities and other issues in the Draco algorithm, an improved algorithm called Draco-F is proposed in this paper, which is based on state bit indexing and dynamic initialization. Firstly, the Draco-F algorithm extends the period of the selection function and reduces its hardware cost by employing the method of state bit indexing. Secondly, while ensuring the uniform usage of Nonlinear Feedback Shift Register (NFSR) state bits, the Draco-F algorithm further reduces the hardware cost of the algorithm by simplifying the output function. Finally, Draco-F introduces dynamic initialization techniques to prevent key backtracking. Security analysis and software-hardware testing results on the Draco-F algorithm show that, compared to the Draco algorithm, Draco-F avoids the security vulnerabilities in Draco, providing a 128 bit security level with an actual 128 bit internal state. Furthermore, the Draco-F algorithm has higher key stream throughput and a smaller circuit area.
The Draco algorithm is an example of a stream cipher based on the Consisting of the Initial Value and Key-prefix (CIVK) scheme, claiming to have provable security against Time Memory Data TradeOff (TMDTO) attacks. However, its selection function has structural flaws, which have been exploited attackers to provide analyses that break its security boundaries. Addressing the security vulnerabilities and other issues in the Draco algorithm, an improved algorithm called Draco-F is proposed in this paper, which is based on state bit indexing and dynamic initialization. Firstly, the Draco-F algorithm extends the period of the selection function and reduces its hardware cost by employing the method of state bit indexing. Secondly, while ensuring the uniform usage of Nonlinear Feedback Shift Register (NFSR) state bits, the Draco-F algorithm further reduces the hardware cost of the algorithm by simplifying the output function. Finally, Draco-F introduces dynamic initialization techniques to prevent key backtracking. Security analysis and software-hardware testing results on the Draco-F algorithm show that, compared to the Draco algorithm, Draco-F avoids the security vulnerabilities in Draco, providing a 128 bit security level with an actual 128 bit internal state. Furthermore, the Draco-F algorithm has higher key stream throughput and a smaller circuit area.
Available online , doi: 10.11999/JEIT240059
Abstract:
Recently, metasurface antenna technology has raised meticulous attention from scholars in the communications, radar, and antenna communities, owing to its great capability in flexible control of electromagnetic waves. In particular, the active tunable device used in the metasurface antenna element is one of the most significant components that affect the performance of the entire system. In this paper, a 95 to 105 GHz digitally controlled attenuator with 5-bit resolution is designed in a 0.13 μm SiGe BiCMOS process. The attenuator employs two different topological structures, reflective and simplified T-type. The 4 dB and 8 dB reflective attenuation units utilize cross-coupled broadband couplers instead of traditional 3 dB couplers or directional couplers, achieving high attenuation precision and low insertion loss. On the other hand, the 0.5 dB, 1 dB, and 2 dB attenuation units adopt a simplified T-type structure. Furthermore, the utilization of RC positive and negative slope correction networks applied separately to different attenuation units enables phase compensation, significantly improving the additional phase shift of the attenuator. Within the desired frequency range of 95～105 GHz, the attenuator achieves an attenuation range of 0～15.5 dB with a step of 0.5 dB in a compact size of 0.12 mm2. It exhibits a simulated insertion loss below 2.5 dB, a simulated amplitude Root Mean Square (RMS) error less than 0.25 dB, and a simulated phase RMS error is better than 2.2°. The proposed W-band attenuator can serve as a key component empowering the hardware implementation of an integrated Transmit/Receive (T/R) metasurface antenna system with simultaneous radiation and scattering control.
Recently, metasurface antenna technology has raised meticulous attention from scholars in the communications, radar, and antenna communities, owing to its great capability in flexible control of electromagnetic waves. In particular, the active tunable device used in the metasurface antenna element is one of the most significant components that affect the performance of the entire system. In this paper, a 95 to 105 GHz digitally controlled attenuator with 5-bit resolution is designed in a 0.13 μm SiGe BiCMOS process. The attenuator employs two different topological structures, reflective and simplified T-type. The 4 dB and 8 dB reflective attenuation units utilize cross-coupled broadband couplers instead of traditional 3 dB couplers or directional couplers, achieving high attenuation precision and low insertion loss. On the other hand, the 0.5 dB, 1 dB, and 2 dB attenuation units adopt a simplified T-type structure. Furthermore, the utilization of RC positive and negative slope correction networks applied separately to different attenuation units enables phase compensation, significantly improving the additional phase shift of the attenuator. Within the desired frequency range of 95～105 GHz, the attenuator achieves an attenuation range of 0～15.5 dB with a step of 0.5 dB in a compact size of 0.12 mm2. It exhibits a simulated insertion loss below 2.5 dB, a simulated amplitude Root Mean Square (RMS) error less than 0.25 dB, and a simulated phase RMS error is better than 2.2°. The proposed W-band attenuator can serve as a key component empowering the hardware implementation of an integrated Transmit/Receive (T/R) metasurface antenna system with simultaneous radiation and scattering control.
Available online , doi: 10.11999/JEIT240297
Abstract:
With the continuous emergence of new applications, the issue of spectrum congestion is becoming increasingly severe. Dual-Functional Radar-Communication (DFRC) is consideredas a key enabling technology for many emerging applications and is one of the essential approaches to addressing the issue of spectrum congestion. However, how to solve the mutual interference between communication and radar and achieve high communication rate is a fundamental challenge that urgently needs to be solved in DFRC system. Based on multi carrier complementary coded division multiple access technology, a DFRC signal suitable for multi-user scenarios is designed in this paper. Theoretical analysis and simulation results show that compared with typical spread spectrum schemes, the proposed scheme can achieve non-interference transmission between communication and radar, with low bit error rate and high user communication rate.
With the continuous emergence of new applications, the issue of spectrum congestion is becoming increasingly severe. Dual-Functional Radar-Communication (DFRC) is consideredas a key enabling technology for many emerging applications and is one of the essential approaches to addressing the issue of spectrum congestion. However, how to solve the mutual interference between communication and radar and achieve high communication rate is a fundamental challenge that urgently needs to be solved in DFRC system. Based on multi carrier complementary coded division multiple access technology, a DFRC signal suitable for multi-user scenarios is designed in this paper. Theoretical analysis and simulation results show that compared with typical spread spectrum schemes, the proposed scheme can achieve non-interference transmission between communication and radar, with low bit error rate and high user communication rate.
Available online , doi: 10.11999/JEIT240083
Abstract:
In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
Available online , doi: 10.11999/JEIT240051
Abstract:
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
General low-complexity joint beamforming designs are proposed for Reconfigurable Intelligent Surface (RIS) assisted multi-user systems. First, the non-convex optimization problem of joint beamforming design is analyzed to maximize sum data rate for RIS-aided multi-user systems. Second, the RIS reflection matrix is designed by using the approximation orthogonality of the beam steering vectors, and the transmit beamforming at the base station is derived from the zero forcing method, and the power allocation is optimized for multiple users. Finally, it is found that the proposed scheme has wide applicability and an order of magnitude reduction on computational complexity than that of existing work. Numerical results show that the proposed beamforming design can achieve high sum data rate, which can be further improved by employing the optimal power allocation. Besides, both the simulation results and theoretical analysis indicate that the sum data rate changes with the RIS location, which provides reference standards for the selection of RIS location.
Available online , doi: 10.11999/JEIT221203
Abstract:
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
To overcome the limitation of the Federated Learning (FL) when the data and model of each client are all heterogenous and improve the accuracy, a personalized Federated learning algorithm with Collation game and Knowledge distillation (pFedCK) is proposed. Firstly, each client uploads its soft-predict on public dataset and download the most correlative of the k soft-predict. Then, this method apply the shapley value from collation game to measure the multi-wise influences among clients and quantify their marginal contribution to others on personalized learning performance. Lastly, each client identify it’s optimal coalition and then distill the knowledge to local model and train on private dataset. The results show that compared with the state-of-the-art algorithm, this approach can achieve superior personalized accuracy and can improve by about 10%.
Available online , doi: 10.11999/JEIT210265
Abstract:
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
The application of Frequency Diverse Array and Multiple Input Multiple Output (FDA-MIMO) radar to achieve range-angle estimation of target has attracted more and more attention. The FDA can simultaneously obtain the degree of freedom of transmitting beam pattern in angle and range. However, its performance is degraded due to the periodicity and time-varying of the beam pattern. Therefore, an improved Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm to estimate the target’s parameters based on a new waveform synthesis model of the Time Modulation and Range Compensation FDA-MIMO (TMRC-FDA-MIMO) radar is proposed. Finally, the proposed method is compared with identical frequency increment FDA-MIMO radar system, logarithmically increased frequency offset FDA-MIMO radar system and MUltiple SIgnal Classification (MUSIC) algorithm through the Cramer Rao lower bound and root mean square error of range and angle estimation, and the excellent performance of the proposed method is verified.
Available online , doi: 10.11999/JEIT240502
Abstract:
To address the issues of significant target scale variation, edge discontinuity, and blurring in 360° omnidirectional images Salient Object Detection (SOD), a method based on the Adjacent Coordination Network (ACoNet) is proposed. First, an adjacent detail fusion module is used to capture detailed and edge information from adjacent features, which facilitates accurate localization of salient objects. Then, a semantic-guided feature aggregation module is employed to aggregate semantic feature information from different scales between shallow and deep features, suppressing the noise transmitted by shallow features. This helps alleviate the problem of discontinuous salient objects and blurred boundaries between the object and background in the decoding stage. Additionally, a multi-scale semantic fusion submodule is constructed to enlarge the receptive field across different convolution layers, thereby achieving better training of the salient object boundaries. Extensive experimental results on two public datasets demonstrate that, compared to 13 other advanced methods, the proposed approach achieves significant improvements in six objective evaluation metrics. Moreover, the subjective visualized detection results show better edge contours and clearer spatial structural details of the salient maps.
To address the issues of significant target scale variation, edge discontinuity, and blurring in 360° omnidirectional images Salient Object Detection (SOD), a method based on the Adjacent Coordination Network (ACoNet) is proposed. First, an adjacent detail fusion module is used to capture detailed and edge information from adjacent features, which facilitates accurate localization of salient objects. Then, a semantic-guided feature aggregation module is employed to aggregate semantic feature information from different scales between shallow and deep features, suppressing the noise transmitted by shallow features. This helps alleviate the problem of discontinuous salient objects and blurred boundaries between the object and background in the decoding stage. Additionally, a multi-scale semantic fusion submodule is constructed to enlarge the receptive field across different convolution layers, thereby achieving better training of the salient object boundaries. Extensive experimental results on two public datasets demonstrate that, compared to 13 other advanced methods, the proposed approach achieves significant improvements in six objective evaluation metrics. Moreover, the subjective visualized detection results show better edge contours and clearer spatial structural details of the salient maps.
Available online , doi: 10.11999/JEIT240289
Abstract:
In the process of trajectory deception against the networked radar using an Unmanned Aerial Vehicle (UAV) cluster, false target points are generated by delaying and forwarding intercepted radar signals. Errors such as radar station location errors, UAV jitter errors, and forwarding delay errors can all cause these false target points to deviate from their intended positions, thereby degrading the effectiveness of the deception. Considering known radar measurement positions, UAV preset positions, deception distances, and a specific Space Resolution Cell (SRC) of the networked radar, the boundary condition of successfully deceiving networked radar by a UAV cluster is analyzed in this paper. The impact patterns of these errors on deception effectiveness are also summarized in the paper. The numerical simulation results show that when all three kinds of errors are present, the derived results can effectively evaluate the deception ability of the UAV cluster to the networked radar.
In the process of trajectory deception against the networked radar using an Unmanned Aerial Vehicle (UAV) cluster, false target points are generated by delaying and forwarding intercepted radar signals. Errors such as radar station location errors, UAV jitter errors, and forwarding delay errors can all cause these false target points to deviate from their intended positions, thereby degrading the effectiveness of the deception. Considering known radar measurement positions, UAV preset positions, deception distances, and a specific Space Resolution Cell (SRC) of the networked radar, the boundary condition of successfully deceiving networked radar by a UAV cluster is analyzed in this paper. The impact patterns of these errors on deception effectiveness are also summarized in the paper. The numerical simulation results show that when all three kinds of errors are present, the derived results can effectively evaluate the deception ability of the UAV cluster to the networked radar.
Available online , doi: 10.11999/JEIT240505
Abstract:
At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
At the present stage, the satellite subsystems in Space Information Networks (SINs) have their own systems and are separated from each other, which makes the network appear closed and fragmented, forming a severe resource barrier and resulting in weak collaborative application ability of space resources and low network expansion ability. The traditional architecture design adopts the “completely subversive” idea of the current space networks, which greatly increases the difficulty of actual deployment. Therefore, based on the current status of satellite networks, the idea of “upgrading step by step” is adopted to promote the evolution of the existing network architecture, and a dynamic and scalable architecture model is proposed in SINs from the perspective of mission drive, so as to realize the efficient and dynamic sharing of space resources among subsystems and promote the dynamic and efficient aggregation of space resources according to the changes in mission requirements. Firstly, a phased network architecture model is proposed, aiming at compatibility and upgrading of the existing network architecture. Then, the design of the core component coordinator is introduced, including network structure and working protocol, superframe structure, and efficient network resource allocation strategy, to realize the efficient transmission of spatial data. The simulation results show that the proposed network architecture realizes the efficient sharing of network resources and greatly improves the utilization rate of network resources.
Available online , doi: 10.11999/JEIT240574
Abstract:
With the rapid development of 6G technology and the evolution of the Industrial Internet of Things (IIoT), federated learning has gained significant attention in the industrial sector. This paper has explored the development and application potential of federated learning in 6G-driven IIoT, analyzing 6G's prospects and how its high speed, low latency, and reliability can support data privacy, resource optimization, and intelligent decision-making. First, existing related work is summarized, and the development requirements along with the vision for applying federated learning technology in 6G industrial IoT scenarios are outlined. Based on this, a new paradigm for industrial federated learning, featuring a hierarchical cross-domain architecture, is proposed to integrate 6G and digital twin technologies, enabling ubiquitous, flexible, and layered federated learning. This supports on-demand and reliable distributed intelligent services in typical Industrial IoT scenarios, achieving the integration of Operational and Communication Information Technology (OCIT). Next, the potential research challenges that federated learning might face towards 6G industrial IoT(6G IIoT-FL) are analyzed and summarized, followed by potential solutions or recommendations. Finally, relevant future directions worth attention in this field are highlighted in the study, with the aim of providing insights for subsequent research to some extent.
With the rapid development of 6G technology and the evolution of the Industrial Internet of Things (IIoT), federated learning has gained significant attention in the industrial sector. This paper has explored the development and application potential of federated learning in 6G-driven IIoT, analyzing 6G's prospects and how its high speed, low latency, and reliability can support data privacy, resource optimization, and intelligent decision-making. First, existing related work is summarized, and the development requirements along with the vision for applying federated learning technology in 6G industrial IoT scenarios are outlined. Based on this, a new paradigm for industrial federated learning, featuring a hierarchical cross-domain architecture, is proposed to integrate 6G and digital twin technologies, enabling ubiquitous, flexible, and layered federated learning. This supports on-demand and reliable distributed intelligent services in typical Industrial IoT scenarios, achieving the integration of Operational and Communication Information Technology (OCIT). Next, the potential research challenges that federated learning might face towards 6G industrial IoT(6G IIoT-FL) are analyzed and summarized, followed by potential solutions or recommendations. Finally, relevant future directions worth attention in this field are highlighted in the study, with the aim of providing insights for subsequent research to some extent.
Available online , doi: 10.11999/JEIT201066
Abstract:
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
To solve the problem of Group Repetition Interval (GRI) selection in the construction of the enhanced LORAN (eLORAN) system supplementary transmission station, a screening algorithm based on cross interference rate is proposed mainly from the mathematical point of view. Firstly, this method considers the requirement of second information, and on this basis, conducts a first screening by comparing the mutual Cross Rate Interference (CRI) with the adjacent Loran-C stations in the neighboring countries. Secondly, a second screening is conducted through permutation and pairwise comparison. Finally, the optimal GRI combination scheme is given by considering the requirements of data rate and system specification. Then, in view of the high-precision timing requirements for the new eLORAN system, an optimized selection is made in multiple optimal combinations. The analysis results show that the average interference rate of the optimal combination scheme obtained by this algorithm is comparable to that between the current navigation chains and can take into account the timing requirements, which can provide referential suggestions and theoretical basis for the construction of high-precision ground-based timing system.
