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学者姓名：陈由甲
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To break through the topological restriction imposed by conventional reflecting/transmitting-only reconfigurable intelligent surface (RIS) in covert communication systems, a simultaneously transmitting and reflecting RIS (STAR-RIS) is adopted in this paper. A transmitter Alice communicates with both users Willie and Bob, where Bob is the covert receiver. Moreover, Willie also plays a warden seeking to detect the covert transmission since it forbids Alice from illegally using the communication resources like energy and bandwidth allocated for them. To obtain the maximum covert rate, we first design the transmission schemes for Alice in the case of sending and not sending covert information and further derive the necessary conditions for Alice to perform covert communication. We also deduce Willie's detection error probability, the minimum value of which obtained as well in terms of an optimal detection threshold. Furthermore, through the design of Alice's transmit power for covert transmission together with transmission and reflection beamforming at STAR-RIS, we achieve the maximum effective covert rate. Our numerical results show the correctness of the proposed theorems and indicate that utilizing STAR-RIS to enhance covert communication is feasible and effective.
Keyword ：
Covert communication Covert communication Noise uncertainty Noise uncertainty Reconfigurable intelligent surface Reconfigurable intelligent surface Transmission scheme Transmission scheme
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| GB/T 7714 | Hu, Jinsong , Cheng, Beixi , Chen, Youjia et al. Simultaneously transmitting and reflecting (STAR) RIS enhanced covert transmission with noise uncertainty [J]. | SIGNAL PROCESSING , 2025 , 232 . |
| MLA | Hu, Jinsong et al. "Simultaneously transmitting and reflecting (STAR) RIS enhanced covert transmission with noise uncertainty" . | SIGNAL PROCESSING 232 (2025) . |
| APA | Hu, Jinsong , Cheng, Beixi , Chen, Youjia , Wang, Jun , Shu, Feng , Chen, Zhizhang . Simultaneously transmitting and reflecting (STAR) RIS enhanced covert transmission with noise uncertainty . | SIGNAL PROCESSING , 2025 , 232 . |
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Inter-cell interference poses a significant challenge to the performance and reliability of cellular networks due to the complex spatial and temporal relationships between network nodes. Addressing this issue requires accurate prediction and assessment of interference. This paper presents a novel solution leveraging the strengths of a weighted graph convolutional network (WGCN) combined with graph coloring techniques. Specifically, we propose a WGCN-based interference estimation model to accurately derive the real-time inter-cell interference. Then, a graph multi-coloring problem is considered for the interference coordination. To address the color collision between cells and the color (i.e. spectrum resources) requirement of individual cells in the graph coloring problem, we propose a WGCN-assisted graph multi-coloring (WGCN-GMC) algorithm to allocate spectrum resources rationally. Simulation results demonstrate that our approach significantly enhances interference coordination, and achieves an impressive average improvement of 58.2 % compared to the traditional GMC algorithm leading to improved overall network performance. © 2025 IEEE.
Keyword ：
Ad hoc networks Ad hoc networks Bayesian networks Bayesian networks Graph algorithms Graph algorithms Network theory (graphs) Network theory (graphs) Wireless sensor networks Wireless sensor networks
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| GB/T 7714 | Wang, Xidian , Guo, Boyang , Liang, Hong et al. Interference Coordination Leveraging Weighted Graph Convolutional Network [C] . 2025 . |
| MLA | Wang, Xidian et al. "Interference Coordination Leveraging Weighted Graph Convolutional Network" . (2025) . |
| APA | Wang, Xidian , Guo, Boyang , Liang, Hong , Jia, Zihan , Chen, Youjia . Interference Coordination Leveraging Weighted Graph Convolutional Network . (2025) . |
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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 autonomous aerial vehicles (AAVs), 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 AAVs' 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 article, the cache refreshing cycle and content placement are jointly optimized in the cache-enabled AAV-assisted vehicular integrated networks (CAVINs) to minimize the content Age of Information (AoI) and energy consumption of the macro AAV. Since the joint optimization problem is variational coupled with nonconvex 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.
Keyword ：
Age of Information (AoI) Age of Information (AoI) Autonomous aerial vehicles Autonomous aerial vehicles Complexity theory Complexity theory Energy consumption Energy consumption Energy efficiency Energy efficiency Information age Information age Internet of Vehicles Internet of Vehicles Internet of Vehicles (IoV) Internet of Vehicles (IoV) mobile edge caching (MEC) mobile edge caching (MEC) Optimization Optimization Real-time systems Real-time systems Simulation Simulation unmanned aerial vehicles (AAVs)-assisted networks unmanned aerial vehicles (AAVs)-assisted networks Vehicle dynamics Vehicle dynamics
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| GB/T 7714 | Xiao, Yang , Lin, Zhijian , Cao, Xiaoxiao et al. AoI Energy-Efficient Edge Caching in AAV-Assisted Vehicular Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (6) : 6764-6774 . |
| MLA | Xiao, Yang et al. "AoI Energy-Efficient Edge Caching in AAV-Assisted Vehicular Networks" . | IEEE INTERNET OF THINGS JOURNAL 12 . 6 (2025) : 6764-6774 . |
| APA | Xiao, Yang , Lin, Zhijian , Cao, Xiaoxiao , Chen, Youjia , Lu, Xiaoqiang . AoI Energy-Efficient Edge Caching in AAV-Assisted Vehicular Networks . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (6) , 6764-6774 . |
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The flexible control of electromagnetic (EM) waves across the entire spatial domain is a long-standing aspiration in metasurface research, driven by its potential to enhance signal coverage and channel capacity. However, most existing full-space metasurfaces are restricted to manipulating incidence within one specific half-space, failing to exploit the EM potential across the entire space. This paper introduces a novel bidirectional transmission-reflection-integrated metasurface (BTRIM) for simultaneous and independent control of full-space incident waves. By dynamically adjusting diode states, the BTRIM can switch among simultaneous and independent forward/backward reflection, forward transmission-reflection (TR), and backward TR functions, each with an independent 1-bit phase response. The core innovation lies in integrating transmission and reflection within a single structure, enabling the metasurface to function at the same frequency and polarization within a compact design. Simulations and experimental validation are conducted to demonstrate BTRIM's ability to implement various wave functions and enhance signal intensity for users in both indoor and outdoor environments. The agreement between simulation and experimental results validates the BTRIM's capacity to simultaneously and independently regulate EM waves from all spatial directions, offering new insights into full-space wave manipulation. This breakthrough creates opportunities for applications in EM sensing, channel enhancement, and next-generation communication systems.
Keyword ：
bidirectional programmable metasurface bidirectional programmable metasurface independent and simultaneous control independent and simultaneous control transmission-reflection-integrated transmission-reflection-integrated
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| GB/T 7714 | Yang, Jun , Li, Yin , Wu, Jun Wei et al. Transmission-Reflection-Integrated Programmable Metasurface for Simultaneous and Independent Control of Bidirectional Incident Waves [J]. | ADVANCED FUNCTIONAL MATERIALS , 2025 , 35 (20) . |
| MLA | Yang, Jun et al. "Transmission-Reflection-Integrated Programmable Metasurface for Simultaneous and Independent Control of Bidirectional Incident Waves" . | ADVANCED FUNCTIONAL MATERIALS 35 . 20 (2025) . |
| APA | Yang, Jun , Li, Yin , Wu, Jun Wei , Dai, Jun Yan , Wang, Si Ran , Li, Hui Dong et al. Transmission-Reflection-Integrated Programmable Metasurface for Simultaneous and Independent Control of Bidirectional Incident Waves . | ADVANCED FUNCTIONAL MATERIALS , 2025 , 35 (20) . |
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Cell-free networks and reconfigurable intelligent surfaces (RIS) are two promising techniques for future wireless communications. The integration of RIS into cell-free networks, termed RIS-aided cell-free networks, offers the potential to significantly enhance network performance. However, the realization of this potential is constrained by the limited capacities of the fronthaul links. To address this challenge, we investigate the joint design of access point (AP) clustering, transmit and passive beamforming in RIS-aided cell-free networks. The objective is to maximize the weighted sum-rate performance while minimizing the number of clustered APs to alleviate the fronthaul overhead. The problem is formulated in a group sparse manner, employing a mixed zero-norm/two-norm term to represent the number of the clustered APs. To solve this problem, we first approximate the mixed zero-norm/two-norm term by the mixed one-norm/two-norm term and provide its equivalent formulation by introducing receive beamforming vectors and weight parameters. Then, an iterative method is proposed based on the block coordinate descent (BCD) technique, which is guaranteed to converge to a stationary point. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.
Keyword ：
Beamforming Beamforming Iterative methods Iterative methods
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| GB/T 7714 | Xu, Chunmei , Jia, Yuanqi , Chen, Youjia et al. Joint AP Clustering and Beamforming Design for RIS-Aided Cell-Free Networks [J]. | IEEE Transactions on Vehicular Technology , 2025 , 74 (5) : 8315-8320 . |
| MLA | Xu, Chunmei et al. "Joint AP Clustering and Beamforming Design for RIS-Aided Cell-Free Networks" . | IEEE Transactions on Vehicular Technology 74 . 5 (2025) : 8315-8320 . |
| APA | Xu, Chunmei , Jia, Yuanqi , Chen, Youjia , Huang, Wei . Joint AP Clustering and Beamforming Design for RIS-Aided Cell-Free Networks . | IEEE Transactions on Vehicular Technology , 2025 , 74 (5) , 8315-8320 . |
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Hierarchical federated learning (HFL) in wireless networks significantly saves communication resources due to edge aggregation conducted in edge mobile computing (MEC) servers. Taking into account the spatially correlated characteristics of data in wireless networks, in this paper, we analyze the performance of HFL with hybrid data distributions, i.e. intra-MEC independent and identically distributed (IID) and inter-MEC non-IID data samples. We derive the upper bound of the difference between the achieved loss and the minimum one, which reveals the impacts of data heterogeneity and global aggregation frequency on the performance of HFL. On this basis, we propose an algorithm named FedHelo which optimizes the aggregation weights and edge/global aggregation frequencies under the constraints of training delay and clients' energy consumption. Our experiments i) verify the obtained theoretical results; ii) demonstrate the performance improvement achieved by FedHelo with the optimal aggregation weights and training/aggregation frequencies, especially in the scenario with high data heterogeneity; and iii) show the preference for edge aggregation in the scenario with a tight delay or client's energy constraint.
Keyword ：
aggregation weight design aggregation weight design Data models Data models Delays Delays energy consumption energy consumption Energy consumption Energy consumption Federated learning Federated learning Hierarchical federated learning Hierarchical federated learning non-IID data non-IID data Servers Servers Training Training training latency training latency Wireless networks Wireless networks
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| GB/T 7714 | Ye, Yuchuan , Chen, Youjia , Yang, Junnan et al. FedHelo: Hierarchical Federated Learning With Loss-Based-Heterogeneity in Wireless Networks [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) : 6066-6079 . |
| MLA | Ye, Yuchuan et al. "FedHelo: Hierarchical Federated Learning With Loss-Based-Heterogeneity in Wireless Networks" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 6 (2024) : 6066-6079 . |
| APA | Ye, Yuchuan , Chen, Youjia , Yang, Junnan , Ding, Ming , Cheng, Peng , Zheng, Haifeng . FedHelo: Hierarchical Federated Learning With Loss-Based-Heterogeneity in Wireless Networks . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) , 6066-6079 . |
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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.
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, Youjia , Zheng, Yuyang , Xu, Jian et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) : 6493-6504 . |
| MLA | Chen, Youjia et al. "Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 21 . 6 (2024) : 6493-6504 . |
| APA | Chen, Youjia , Zheng, Yuyang , Xu, Jian , Lin, Hanyu , Cheng, Peng , Ding, Ming et al. Knowledge-Assisted Resource Allocation With Domain Adversarial Neural Networks . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) , 6493-6504 . |
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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.
Keyword ：
6G 6G Atmospheric modeling Atmospheric modeling Computational modeling Computational modeling Downlink Downlink federated learning (FL) federated learning (FL) integrated sensing and communication (ISAC) integrated sensing and communication (ISAC) Optimization Optimization over-the-air computation (AirComp) over-the-air computation (AirComp) Radar Radar Task analysis Task analysis Uplink Uplink
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| GB/T 7714 | Du, Mengxuan , Zheng, Haifeng , Gao, Min 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) : 35551-35567 . |
| MLA | Du, Mengxuan 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) : 35551-35567 . |
| APA | Du, Mengxuan , Zheng, Haifeng , Gao, Min , Feng, Xinxin , Hu, Jinsong , Chen, Youjia . Integrated Sensing, Communication, and Computation for Over-the-Air Federated Learning in 6G Wireless Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (21) , 35551-35567 . |
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Hierarchical federated learning (HFL) in wireless networks significantly saves communication resources thanks to edge aggregation in edge mobile computing (MEC) servers. Considering the spatially correlated data in wireless networks, in this paper, we analyze the performance of HFL with hybrid data distributions, i.e. intra-MEC independent and identically distributed (IID) and inter-MEC non-IID data samples. We also derive the performance impacts of data heterogeneity and global aggregation interval. Based on our theoretical results, we further propose a novel aggregation weights design with loss-based heterogeneity to accelerate the training of HFL and improve learning accuracy. Our simulations verify the theoretical results and demonstrate the performance gain achieved by the proposed aggregation weights design. Moreover, we find that the performance gain of the proposed aggregation weights design is higher in a high-heterogeneity scenario than in a low-heterogeneity one.
Keyword ：
aggregation weights design aggregation weights design Hierarchical federated learning Hierarchical federated learning non-IID data non-IID data wireless networks wireless networks
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| GB/T 7714 | Ye, Yuchuan , Chen, Youjia , Yang, Junnan et al. Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 , 2024 . |
| MLA | Ye, Yuchuan et al. "Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 (2024) . |
| APA | Ye, Yuchuan , Chen, Youjia , Yang, Junnan , Ding, Ming , Cheng, Peng , Hu, Jinsong et al. Wireless Hierarchical Federated Aggregation Weights Design with Loss-Based-Heterogeneity . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS, INFOCOM WKSHPS 2024 , 2024 . |
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This work investigates the frequency diverse array (FDA)-assisted covert communication system, in which the general beampattern generated by FDA is utilized to establish a secure region for the legitimate user, thereby improving the system's covert performance. Specifically, we first derive a closed-form expression of the system covertness constraint based on Kullback-Leibler (KL) divergence. Then, when the FDA beam-pattern power attenuates to a value that satisfies the covertness constraint, the secure region is defined and the corresponding boundary expression of which is also deduced. Furthermore, to reduce the risk of covert transmission being detected, the secure region minimization problem is established, while the methods based on the Rayleigh-Ritz theorem and nonlinear programming are formulated to solve the optimization problem, respectively. Simulation results compare the different frequency schemes and show that the optimized frequency leads to a smaller area of the secure region and lower KL divergence than the benchmark schemes.
Keyword ：
Covert communications Covert communications finite blocklength finite blocklength frequency diverse array frequency diverse array
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| GB/T 7714 | Zhou, Yiting , Hu, Jinsong , Chen, Youjia et al. Establishing Secure Region for Covert Communication Based on Frequency Diverse Array [J]. | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| MLA | Zhou, Yiting et al. "Establishing Secure Region for Covert Communication Based on Frequency Diverse Array" . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC (2024) . |
| APA | Zhou, Yiting , Hu, Jinsong , Chen, Youjia , Wang, Jun , Shu, Feng , Chen, Zhizhang (David) . Establishing Secure Region for Covert Communication Based on Frequency Diverse Array . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
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