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学者姓名：胡锦松
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Abstract ：
Efficient and fair resource allocation is a critical challenge in vehicular networks, especially under high mobility and unknown channel state information (CSI). Existing works mainly focus on centralized optimization with perfect CSI or decentralized heuristics with partial CSI, which may not be practical or effective in real-world scenarios. In this paper, we propose a novel hierarchical deep reinforcement learning (HDRL) framework to address the joint channel and power allocation problem in vehicular networks with high mobility and unknown CSI. The main contributions of this work are twofold. Firstly, this paper develops a multi-agent reinforcement learning architecture that integrates both centralized training with global information and decentralized execution with local observations. The proposed architecture leverages the strengths of deep Q-networks (DQN) for discrete channel selection and deep deterministic policy gradient (DDPG) for continuous power control while learning robust and adaptive policies under time-varying channel conditions. Secondly, this paper designs efficient reward functions and training algorithms that encourage cooperation among vehicles and balance the trade-off between system throughput and individual fairness. By incorporating Jain's fairness index into the reward design and adopting a hybrid experience replay strategy, the proposed algorithm achieves a good balance between system efficiency and user equity. Extensive simulations demonstrate the superiority of the proposed HDRL method over state-of-the-art benchmarks, including DQN, DDPG, and fractional programming, in terms of both average throughput and fairness index under various realistic settings. The proposed framework provides a promising solution for intelligent and efficient resource management in future vehicular networks.
Keyword ：
Cognitive internet of vehicles Cognitive internet of vehicles Deep reinforcement learning Deep reinforcement learning Resource allocation Resource allocation Unknown channel state information Unknown channel state information
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| GB/T 7714 | Wang, Jun , Jiang, Weibin , Xu, Haodong et al. A novel resource allocation method based on hierarchical deep reinforcement learning for cognitive internet of vehicles with unknown channel state information [J]. | COMPUTER NETWORKS , 2025 , 264 . |
| MLA | Wang, Jun et al. "A novel resource allocation method based on hierarchical deep reinforcement learning for cognitive internet of vehicles with unknown channel state information" . | COMPUTER NETWORKS 264 (2025) . |
| APA | Wang, Jun , Jiang, Weibin , Xu, Haodong , Hu, Jinsong , Wu, Liang , Shu, Feng et al. A novel resource allocation method based on hierarchical deep reinforcement learning for cognitive internet of vehicles with unknown channel state information . | COMPUTER NETWORKS , 2025 , 264 . |
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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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针对反向散射系统中的隐蔽通信问题,提出一种基于频谱共享的反向散射隐蔽通信方案.在该方案中,次发射机(secondary transmitter,ST)将隐蔽信息调制到主发射机(primary transmitter,PT)的信号上,并通过反向散射以实现隐蔽传输.首先,给出了ST复反射系数的表达式.其次,推导出监测者(Willie)二元假设检验的最优检测阈值以及对应的最小检测错误概率.考虑瑞利衰落信道,对 ST 的隐蔽传输性能进行了分析,得到有效隐蔽传输速率(effective covert rate,ECR)表达式.然后,给出隐蔽传输过程中的误码率表达式.最后,在满足约束条件的前提下,通过联合优化PT的发射功率以及ST的复反射系数以最大化ECR.实验结果表明,利用瑞利衰落信道的信道不确定性,可以在该反向散射系统中实现隐蔽传输.在隐蔽传输过程中,Willie 的最小检测错误概率仅与 ST 的复反射系数相关.此外,所提出的优化方案能够有效提升该系统的隐蔽通信性能.
Keyword ：
二元假设检验 二元假设检验 反向散射通信 反向散射通信 隐蔽通信 隐蔽通信 频谱共享 频谱共享
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| GB/T 7714 | 胡锦松 , 罗龙发 , 李鸿炜 et al. 面向共享频谱的反向散射隐蔽通信设计与实验分析 [J]. | 实验技术与管理 , 2025 , 42 (6) : 112-118 . |
| MLA | 胡锦松 et al. "面向共享频谱的反向散射隐蔽通信设计与实验分析" . | 实验技术与管理 42 . 6 (2025) : 112-118 . |
| APA | 胡锦松 , 罗龙发 , 李鸿炜 , 陈由甲 , 郑海峰 . 面向共享频谱的反向散射隐蔽通信设计与实验分析 . | 实验技术与管理 , 2025 , 42 (6) , 112-118 . |
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Super-Resolution (SR) algorithms aim to enhance the resolutions of images. Massive deep-learning-based SR techniques have emerged in recent years. In such case, a visually appealing output may contain additional details compared with its reference image. Accordingly, fully referenced Image Quality Assessment (IQA) cannot work well; however, reference information remains essential for evaluating the qualities of SR images. This poses a challenge to SR-IQA: How to balance the referenced and no-reference scores for user perception? In this paper, we propose a Perception-driven Similarity-Clarity Tradeoff (PSCT) model for SR-IQA. Specifically, we investigate this problem from both referenced and no-reference perspectives, and design two deep-learning-based modules to obtain referenced and no-reference scores. We present a theoretical analysis based on Human Visual System (HVS) properties on their tradeoff and also calculate adaptive weights for them. Experimental results indicate that our PSCT model is superior to the state-of-the-arts on SR-IQA. In addition, the proposed PSCT model is also capable of evaluating quality scores in other image enhancement scenarios, such as deraining, dehazing and underwater image enhancement. The source code is available at https://github.com/kekezhang112/PSCT.
Keyword ：
Adaptation models Adaptation models Distortion Distortion Feature extraction Feature extraction Image quality assessment Image quality assessment image super-resolution image super-resolution Measurement Measurement perception-driven perception-driven Quality assessment Quality assessment similarity-clarity tradeoff similarity-clarity tradeoff Superresolution Superresolution Task analysis Task analysis
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| GB/T 7714 | Zhang, Keke , Zhao, Tiesong , Chen, Weiling et al. Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (7) : 5897-5907 . |
| MLA | Zhang, Keke et al. "Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 7 (2024) : 5897-5907 . |
| APA | Zhang, Keke , Zhao, Tiesong , Chen, Weiling , Niu, Yuzhen , Hu, Jinsong , Lin, Weisi . Perception-Driven Similarity-Clarity Tradeoff for Image Super-Resolution Quality Assessment . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (7) , 5897-5907 . |
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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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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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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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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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The high-speed movement of Vehicle Users (VUs) in Cognitive Internet of Vehicles (CIoV) causes rapid changes in users location and path loss. In the case of imperfect control channels, the influence of high-speed movement increases the probability of error in sending local spectrum sensing decisions by VUs. On the other hand, Malicious Vehicle Users (MVUs) can launch Spectrum Sensing Data Falsification (SSDF) attacks to deteriorate the spectrum sensing decisions, mislead the final spectrum sensing decisions of Collaborative Spectrum Sensing (CSS), and bring serious security problems to the system. In addition, the high-speed movement can increases the concealment of the MVUs. In this paper, we study the scenario of VUs moving at high speeds, and data transmission in an imperfect control channel, and propose a blockchain-based method to defend against massive SSDF attacks in CIoV networks to prevennt independent and cooperative attacks from MVUs. The proposed method combines blockchain with spectrum sensing and spectrum access, abandons the decision-making mechanism of the Fusion Center (FC) in the traditional CSS, adopts distributed decision-making, and uses Prospect Theory (PT) modeling in the decision-making process, effectively improves the correct rate of final spectrum sensing decision in the case of multiple attacks. The local spectrum sensing decisions of VUs are packaged into blocks and uploaded after the final decision to achieve more accurate and secure spectrum sensing, and then identify MVUs by the reputation value. In addition, a smart contract that changes the mining difficulty of VUs based on their reputation values is proposed. It makes the mining difficulty of MVUs more difficult and effectively limits MVUs' access to the spectrum band. The final simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Keyword ：
blockchain blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data communication Data communication Decision making Decision making History History Internet of Vehicles Internet of Vehicles prospect theory (PT) prospect theory (PT) Sensors Sensors smart contract smart contract Smart contracts Smart contracts spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack
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| GB/T 7714 | Lin, Ruiquan , Li, Fushuai , Wang, Jun et al. A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) : 6954-6967 . |
| MLA | Lin, Ruiquan et al. "A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 73 . 5 (2024) : 6954-6967 . |
| APA | Lin, Ruiquan , Li, Fushuai , Wang, Jun , Hu, Jinsong , Zhang, Zaichen , Wu, Liang . A Blockchain-Based Method to Defend Against Massive SSDF Attacks in Cognitive Internet of Vehicles . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2024 , 73 (5) , 6954-6967 . |
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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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