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学者姓名：陈由甲
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Abstract ：
Given the popularity of flawless telepresence and the resultants explosive growth of wireless video applications, besides handling the traffic surge, satisfying the demanding user requirements for video qualities has become another important goal of network operators. Inspired by this, cooperative edge caching intrinsically amalgamated with scalable video coding is investigated. Explicitly, the concept of a Pareto-optimal semi-distributed multiagent multipolicy deep reinforcement learning (SD-MAMP-DRL) algorithm is conceived for managing the cooperation of heterogeneous network nodes. To elaborate, a multipolicy reinforcement learning algorithm is proposed for finding the Pareto-optimal policies during the training phase, which balances the teletraffic versus the user experience tradeoff. Then the optimal policy/solution can be activated during the execution phase by appropriately selecting the associated weighting coefficient according to the dynamically fluctuating network traffic load. Our experimental results show that the proposed SD-MAMP- acrshort DRL algorithm: 1) achieves better performance than the benchmark algorithms and 2) obtains a near-complete Pareto front in various scenarios and selects the optimal solution by adaptively adjusting the above-mentioned pair of objectives.
Keyword ：
Cooperative caching Cooperative caching Costs Costs Edge caching Edge caching multiagent reinforcement learning (MARL) multiagent reinforcement learning (MARL) multiobjective optimization multiobjective optimization Pareto front Pareto front Quality of experience Quality of experience Reinforcement learning Reinforcement learning scalable video coding (SVC) scalable video coding (SVC) Servers Servers Training Training Wireless communication Wireless communication
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| GB/T 7714 | Guo, Boyang , Chen, Youjia , Cheng, Peng et al. Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement Learning [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) : 7904-7917 . |
| MLA | Guo, Boyang et al. "Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement Learning" . | IEEE INTERNET OF THINGS JOURNAL 11 . 5 (2024) : 7904-7917 . |
| APA | Guo, Boyang , Chen, Youjia , Cheng, Peng , Ding, Ming , Hu, Jinsong , Hanzo, Lajos . Pareto-Optimal Multiagent Cooperative Caching Relying on Multipolicy Reinforcement Learning . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) , 7904-7917 . |
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Relying on a data-driven methodology, deep learning has emerged as a new approach for dynamic resource allocation in large-scale cellular networks. This paper proposes a knowledge-assisted domain adversarial network to reduce the number of poorly performing base stations (BSs) by dynamically allocating radio resources to meet real-time mobile traffic needs. Firstly, we calculate theoretical inter-cell interference and BS capacity using Voronoi tessellation and stochastic geometry, which are then incorporated into a neural network as key parameters. Secondly, following the practical assessment, a performance classifier evaluates BS performance based on given traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Our experimental results demonstrate that the proposed knowledge-assisted domain adversarial resource allocation (KDARA) strategy effectively decreases the number of poorly performing BSs in the cellular network, and in turn, outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption. IEEE
Keyword ：
domain adversarial network domain adversarial network Dynamic scheduling Dynamic scheduling knowledge-assisted knowledge-assisted Measurement Measurement Mobile big data Mobile big data Neural networks Neural networks Real-time systems Real-time systems resource allocation resource allocation Resource management Resource management transfer learning transfer learning Transfer learning Transfer learning Wireless networks Wireless networks
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| GB/T 7714 | Chen, Y. , Zheng, Y. , Xu, J. et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks [J]. | IEEE Transactions on Network and Service Management , 2024 , 21 (6) : 1-1 . |
| MLA | Chen, Y. et al. "Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks" . | IEEE Transactions on Network and Service Management 21 . 6 (2024) : 1-1 . |
| APA | Chen, Y. , Zheng, Y. , Xu, J. , Lin, H. , Cheng, P. , Ding, M. et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks . | IEEE Transactions on Network and Service Management , 2024 , 21 (6) , 1-1 . |
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Due to the rapidly increasing number of base stations (BSs) in the operational cellular networks, their energy consumption is escalating. In this paper, we propose an intelligent data-driven BS sleeping mechanism relying on a wireless traffic prediction model that measures the BSs' capacity in different regions. Firstly, a spatio-temporal cellular traffic prediction model is proposed, where a multi-graph convolutional network (MGCN) is developed to capture the associated spatial features. Furthermore, a multi-channel long short-term memory (LSTM) solution involving hourly, daily, and weekly periodic data is used to capture the relevant temporal features. Secondly, the capacities of macro-cell BSs (MBSs) and small-cell BSs (SBSs) having different environment characteristics are modeled, where both clustering and transfer learning algorithms are adopted for quantifying the traffic supported by the MBSs and SBSs. Finally, an optimal BS sleeping strategy is proposed for minimizing the network's power consumption. Experimental results show that the proposed MGCN-LSTM model outperforms the existing models in terms of its cellular traffic prediction accuracy, and the proposed BS sleeping strategy using an approximated non-linear model of the associated capacity function achieves near-maximal energy-saving at a modest complexity.
Keyword ：
BS sleeping BS sleeping Cellular networks Cellular networks cellular traffic prediction cellular traffic prediction Convolution Convolution Convolutional neural networks Convolutional neural networks Energy consumption Energy consumption graph convolutional network graph convolutional network Predictive models Predictive models Real-time systems Real-time systems Roads Roads transfer learning. transfer learning.
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| GB/T 7714 | Lin, Jiansheng , Chen, Youjia , Zheng, Haifeng et al. A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) : 5627-5643 . |
| MLA | Lin, Jiansheng et al. "A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 6 (2024) : 5627-5643 . |
| APA | Lin, Jiansheng , Chen, Youjia , Zheng, Haifeng , Ding, Ming , Cheng, Peng , Hanzo, Lajos . A Data-Driven Base Station Sleeping Strategy Based on Traffic Prediction . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) , 5627-5643 . |
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针对随机分布多跳中继网络存在的通信安全问题，提出了一种基于强化学习的智能路径选择方案。该方案考虑单密钥和独立密钥传输2种情况，分别对监测者的检测性能进行了探究，并基于隐蔽约束确定了各中继的最佳功率分配。最后，基于强化学习技术实现了多跳网络传输路径的智能选择，以保证传输的隐蔽性，并最大化系统的隐蔽吞吐量。结果表明，单密钥方案所选路径倾向于绕开监测者所监测的区域，而独立密钥方案所选路径可以穿过监测者的监测区域，并且独立密钥所能达到的系统增益显著优于单密钥。
Keyword ：
多跳中继 多跳中继 强化学习 强化学习 路径选择 路径选择 隐蔽通信 隐蔽通信
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| GB/T 7714 | 胡锦松 , 吴林梅 , 国明乾 et al. 多跳中继网络隐蔽通信设计与实验分析 [J]. | 实验室研究与探索 , 2024 , 43 (02) : 13-17 . |
| MLA | 胡锦松 et al. "多跳中继网络隐蔽通信设计与实验分析" . | 实验室研究与探索 43 . 02 (2024) : 13-17 . |
| APA | 胡锦松 , 吴林梅 , 国明乾 , 陈由甲 , 赵铁松 . 多跳中继网络隐蔽通信设计与实验分析 . | 实验室研究与探索 , 2024 , 43 (02) , 13-17 . |
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Integrated Sensing and Communication (ISAC) Systems, which can support simultaneous information transmission and target detection, have been regarded as promising solutions for various emerging wireless network applications. In this work, a covert transmission system with the aid of the ISAC technique is proposed, where the radar intends to sense a target and send message to legitimate users covertly against the warden's surveillance with either perfect or imperfect channel state information (CSI) at the warden. We formulate a beamforming optimization problem for the dual-function signal used for sensing and communication to maximize the covert throughput, subject to a covertness constraint, a maximum transmit power constraint, and a radar detection constraint. We then determine the sufficient conditions under which the covert beamforming design problem can be solved by semidefinite relaxation (SDR). Numerical results show that the proposed covert ISAC system can guarantee covert transmission while ensuring a certain level of sensing performance, and there exists a performance trade-off between the considered radar detection and covert transmission.
Keyword ：
beamforming design beamforming design Covert communications Covert communications integrated sensing and communication (ISAC) integrated sensing and communication (ISAC)
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| GB/T 7714 | Hu, Jinsong , Lin, Qingzhuan , Yan, Shihao et al. Covert Transmission via Integrated Sensing and Communication Systems [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (3) : 4441-4446 . |
| MLA | Hu, Jinsong et al. "Covert Transmission via Integrated Sensing and Communication Systems" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73 . 3 (2024) : 4441-4446 . |
| APA | Hu, Jinsong , Lin, Qingzhuan , Yan, Shihao , Zhou, Xiaobo , Chen, Youjia , Shu, Feng . Covert Transmission via Integrated Sensing and Communication Systems . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (3) , 4441-4446 . |
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This work proposes a covert communication scheme in a cognitive radio network where a secondary transmitter (ST) transmits confidential information to a secondary receiver under the cover of jammers with homogeneous Poisson point process. Specifically, we first analyze the detection performance of the primary transmitter (PT) and Willie under collaboration and non-collaboration modes. We then derive the covert transmission outage probability under ST’s correct and incorrect decisions for whether PT transmits or not and obtain the expression for the effective covert rate (ECR). In order to maximize the ECR, we derive the optimal value of the time allocation ratio, based on which, we also derive the optimal value of ST’s transmit power subject to the covertness constraint and some power constraints. Our examination shows the non-collaboration mode outperforms the collaboration mode in terms of achieving a higher ECR, because the uncertainty of the PTs transmission in the former one will cause confusion at Willie and lead to an increased detection error rate. In addition, the proposed scheme effectively increases the ECR when compared with the scheme without the jammer. IEEE
Keyword ：
Autonomous aerial vehicles Autonomous aerial vehicles cognitive radio cognitive radio Collaboration Collaboration Covert communication Covert communication Error analysis Error analysis Interference Interference jammer jammer Jamming Jamming Poisson point process Poisson point process Radio transmitters Radio transmitters Trajectory Trajectory
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| GB/T 7714 | Hu, J. , Li, H. , Chen, Y. et al. Covert Communication in Cognitive Radio Networks with Poisson Distributed Jammers [J]. | IEEE Transactions on Wireless Communications , 2024 , 23 (10) : 1-1 . |
| MLA | Hu, J. et al. "Covert Communication in Cognitive Radio Networks with Poisson Distributed Jammers" . | IEEE Transactions on Wireless Communications 23 . 10 (2024) : 1-1 . |
| APA | Hu, J. , Li, H. , Chen, Y. , Shu, F. , Wang, J. . Covert Communication in Cognitive Radio Networks with Poisson Distributed Jammers . | IEEE Transactions on Wireless Communications , 2024 , 23 (10) , 1-1 . |
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Federated Learning (FL), as a privacy-enhancing distributed learning paradigm, has recently attracted much attention in wireless systems. By providing communication and computation services, the base station (BS) helps participants collaboratively train a shared model without transmitting raw data. Concurrently, with the advent of integrated sensing and communication (ISAC) and the growing demand for sensing services, it is envisioned that BS will simultaneously serve sensing services, as well as communication and computation services, e.g., FL, in future 6G wireless networks. To this end, we provide a novel integrated sensing, communication and computation (ISCC) system, called Fed-ISCC, where BS conducts sensing and FL in the same time-frequency resource, and the over-the-air computation (AirComp) is adopted to enable fast model aggregation. To mitigate the interference between sensing and FL during uplink transmission, we propose a receive beamforming approach. Subsequently, we analyze the convergence of FL in the Fed-ISCC system, which reveals that the convergence of FL is hindered by device selection error and transmission error caused by sensing interference, channel fading and receiver noise. Based on this analysis, we formulate an optimization problem that considers the optimization of transceiver beamforming vectors and device selection strategy, with the goal of minimizing transmission and device selection errors while ensuring the sensing requirement. To address this problem, we propose a joint optimization algorithm that decouples it into two main problems and then solves them iteratively. Simulation results demonstrate that our proposed algorithm is superior to other comparison schemes and nearly attains the performance of ideal FL. IEEE
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink Federated learning Federated learning integrated sensing and communication integrated sensing and communication Optimization Optimization over-the-air computation over-the-air computation Radar Radar Task analysis Task analysis Uplink Uplink
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| GB/T 7714 | Du, M. , Zheng, H. , Gao, M. et al. Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks [J]. | IEEE Internet of Things Journal , 2024 , 11 (21) : 1-1 . |
| MLA | Du, M. et al. "Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks" . | IEEE Internet of Things Journal 11 . 21 (2024) : 1-1 . |
| APA | Du, M. , Zheng, H. , Gao, M. , Feng, X. , Hu, J. , Chen, Y. . Integrated Sensing, Communication and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE Internet of Things Journal , 2024 , 11 (21) , 1-1 . |
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Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.
Keyword ：
ADMM ADMM deep unfolding network deep unfolding network integrated sensing and communication integrated sensing and communication Orthogonal Time Frequency Space (OTFS) Orthogonal Time Frequency Space (OTFS) target parameter estimation target parameter estimation
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| GB/T 7714 | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin et al. Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems [J]. | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
| MLA | Lin, Weizhi et al. "Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems" . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 (2024) . |
| APA | Lin, Weizhi , Zheng, Haifeng , Feng, Xinxin , Chen, Youjia . Deep Unfolding Network for Target Parameter Estimation in OTFS-based ISAC Systems . | 2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 , 2024 . |
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The development of the extremely large-scale antenna array (ELAA) for the upcoming 6G technology indicates the significance of near-field communication. This work performs a near-field analysis to improve covertness when maximum ratio transmission (MRT) is employed with ELAA to send messages to the legitimate user. Specifically, we first derive the covertness constraint of the system by analyzing the beampattern. Based on this constraint, we introduce the concept of the vulnerable region, which is the region where covert communication is not achievable if a potential warden resides there. Furthermore, determining the vulnerable region involves deriving the range of distances by initially fixing the angle dimension, and then utilizing the covertness and the minimum effective throughput constraints to obtain the range of angle. The simulation results illustrate the efficacy of the determined vulnerable region in both distance and angle dimensions. As the azimuth angle or the distance between the legitimate user and the transmitter decreases, the area of the vulnerable region decreases. Additionally, increasing the number of warden's antennas or requiring a higher signal-to-noise ratio for legitimate user will expand the vulnerable region. IEEE
Keyword ：
Antennas Antennas Array signal processing Array signal processing Covert communication Covert communication near-field communication near-field communication Signal to noise ratio Signal to noise ratio Throughput Throughput Transmitting antennas Transmitting antennas Vectors Vectors vulnerable region vulnerable region Wireless communication Wireless communication
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| GB/T 7714 | Hu, J. , Zhou, Y. , Zheng, H. et al. Minimizing Vulnerable Region for Near-Field Covert Communication [J]. | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) : 1-6 . |
| MLA | Hu, J. et al. "Minimizing Vulnerable Region for Near-Field Covert Communication" . | IEEE Transactions on Vehicular Technology 73 . 12 (2024) : 1-6 . |
| APA | Hu, J. , Zhou, Y. , Zheng, H. , Chen, Y. , Shu, F. , Wang, J. . Minimizing Vulnerable Region for Near-Field Covert Communication . | IEEE Transactions on Vehicular Technology , 2024 , 73 (12) , 1-6 . |
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Mobile edge caching (MEC) has grown substantially with the rapid development in scale and complexity of data traffic. By exploiting the expansive coverage of unmanned aerial vehicles (UAVs), MEC enables services for massive vehicle users (VUs) simultaneously, which is promising for enhancing network transmission efficiency. Nonetheless, due to challenges arising from the timeliness and freshness of content services caused by UAVs' limited endurance and airborne capacity, caching strategy considering the real-time of content in large-scale dynamic Internet of Vehicles (IoV) environments remains open. With the above consideration, in this paper, the cache refreshing cycle and content placement are jointly optimized in the cache-enabled UAV-assisted vehicular integrated networks (CUVIN) to minimize the content age of information (AoI) and energy consumption of the macro UAV. Since the joint optimization problem is variational coupled with non-convex binary constraints, it is decoupled and solved by a double-iteration method. Specifically, the optimal cache refreshing cycle is derived in semi-closed form with the Karush-Kuhn-Tucker (KKT) conditions. The locally optimal solution of the content placement is obtained through successive convex approximation (SCA). Simulation results corroborate the effectiveness and superiority of the proposed scheme. © 2024 IEEE.
Keyword ：
Age of information (AoI) Age of information (AoI) internet of vehicles (IoV) internet of vehicles (IoV) mobile edge caching (MEC) mobile edge caching (MEC) unmanned aerial vehicles (UAV)-assisted networks unmanned aerial vehicles (UAV)-assisted networks
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| GB/T 7714 | Xiao, Y. , Lin, Z. , Cao, X. et al. AoI-Energy-Efficient Edge Caching in UAV-Assisted Vehicular Networks [J]. | IEEE Internet of Things Journal , 2024 . |
| MLA | Xiao, Y. et al. "AoI-Energy-Efficient Edge Caching in UAV-Assisted Vehicular Networks" . | IEEE Internet of Things Journal (2024) . |
| APA | Xiao, Y. , Lin, Z. , Cao, X. , Chen, Y. , Lu, X. . AoI-Energy-Efficient Edge Caching in UAV-Assisted Vehicular Networks . | IEEE Internet of Things Journal , 2024 . |
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