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学者姓名：陈由甲
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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 . |
| MLA | Yang, Jun et al. "Transmission-Reflection-Integrated Programmable Metasurface for Simultaneous and Independent Control of Bidirectional Incident Waves" . | ADVANCED FUNCTIONAL MATERIALS (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 . |
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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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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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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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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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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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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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In the realm of 6G wireless networks, virtual reality (VR) 360-degree videos stand out as a pivotal application. Researches on the users' quality of experience (QoE) for VR 360-degree videos mainly focus on video coding and transmission schemes, with a limited investigation into the impacts of wireless channels. To fill this gap, this paper emulates VR 360-degree video transmission on three kinds of wireless channels: additive Gaussian white noise (AWGN), Rayleigh fading, and Rician fading channels. The performance metrics for the wireless physical layer including signal-to-noise ratio (SNR), end-to-end delay, and bit error rate are investigated for their impacts on the performance metrics of video transmission, including video bitrate, stalling time, and start-up delay. Finally, a comprehensive QoE score is derived based on measured application-layer quality. Furthermore, we fit the functions: i) a log-scaling law of QoE vs. bandwidth, and ii) a Sigmoid function-scaling law for QoE vs. SNR. The results shed light on guiding physical layer network optimization aimed at improving the subjective QoE of VR videos.
Keyword ：
VR video QoE VR video QoE Wireless link performance Wireless link performance
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| GB/T 7714 | Sun, Shengying , Chen, Youjia , Guo, Boyang et al. Mapping Wireless Link Performance to 360-Degree VR QoE [J]. | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
| MLA | Sun, Shengying et al. "Mapping Wireless Link Performance to 360-Degree VR QoE" . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC (2024) . |
| APA | Sun, Shengying , Chen, Youjia , Guo, Boyang , Ye, Yuchuan , Hu, Jinsong , Zheng, Haifeng . Mapping Wireless Link Performance to 360-Degree VR QoE . | CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA, ICCC , 2024 . |
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