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学者姓名：胡锦松
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Abstract ：
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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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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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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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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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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This article proposes an incomplete information Bayesian Stackelberg game, which is adapted to the Cognitive Internet of Vehicles (CIoVs) network to defend against spectrum sensing data falsification (SSDF) attacks from malicious vehicle users (MVUs). Specifically, this article considers the random appearance of MVUs caused by mobility, intelligent SSDF attacks of MVUs, and the different spectrum sensing performances among vehicle users (VUs). In the game, the fusion center (FC) as the leader aims to improve the global detection performance while effectively identifying the identities of different VUs by optimizing the global decision threshold and the reputation threshold. On the other hand, this article models the random appearance of MVUs as a Poisson random process, and the MVUs are the intelligent followers; they optimize the attack probabilities according to the FC's strategies to evade detection and increase the chance of selfish transmission and the damage to the CIoV network. To solve the MVUs' nonconvex optimization problem, this article uses the successive convex approximation (SCA) technique to obtain MVUs' optimal attack probabilities. For the FC, this article proposes the method combining alternating optimization and SCA to solve the nonconvex optimization problem of the FC and obtain its optimal defense strategies. This article also proves the convergence of the proposed method and the existence of the Stackelberg equilibrium (SE). The simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods.
Keyword ：
Bayes methods Bayes methods Cognitive Internet of Vehicles (CIoVs) Cognitive Internet of Vehicles (CIoVs) Games Games game theory game theory Intelligent sensors Intelligent sensors Internet of Vehicles Internet of Vehicles Optimization Optimization physical layer security physical layer security Random processes Random processes Sensors Sensors spectrum sensing data falsification (SSDF) attacks spectrum sensing data falsification (SSDF) attacks
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| GB/T 7714 | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng et al. Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (19) : 31310-31323 . |
| MLA | Li, Fushuai et al. "Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game" . | IEEE SENSORS JOURNAL 24 . 19 (2024) : 31310-31323 . |
| APA | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng , Wang, Jun , Hu, Jinsong , Shu, Feng . Defending Against SSDF Attacks From Randomly Appearing Intelligent Malicious Vehicle Users in the CIoV Network by Bayesian Stackelberg Game . | IEEE SENSORS JOURNAL , 2024 , 24 (19) , 31310-31323 . |
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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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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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Semantic communication, by the extraction of essential semantic information from source data, reduces data volume and enhances transmission efficiency, making it increasingly popular among candidate technologies in 6G. In this letter, a covert transmission scheme for text semantic communications is proposed, where a transmitter attempts to send text semantic information to a legitimate receiver under the surveillance of a warden, while the receiver emits artificial noise (AN) to interfere the warden. To maximize semantic spectral efficiency, we formulate an optimization problem while considering constraints on covertness, the minimum semantic similarity, and the number of semantic symbols mapped per word K. We derive the closed-form expressions for the optimal transmit power and AN power when K is fixed, and employ a one-dimension searching method to find the optimal K*. Numerical results demonstrate that fixed AN power can contribute to covert transmission and in the semantic model, K* is the minimum allowable K.
Keyword ：
artificial noise artificial noise Covert communication Covert communication Decoding Decoding Electronic mail Electronic mail finite blocklength finite blocklength Receivers Receivers Semantics Semantics Signal to noise ratio Signal to noise ratio Spectral efficiency Spectral efficiency Symbols Symbols text semantic communication text semantic communication
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| GB/T 7714 | Hu, Jinsong , Ye, Longjie , Chen, Youjia et al. Covert Communications for Text Semantic With Finite Blocklength [J]. | IEEE WIRELESS COMMUNICATIONS LETTERS , 2024 , 13 (10) : 2842-2846 . |
| MLA | Hu, Jinsong et al. "Covert Communications for Text Semantic With Finite Blocklength" . | IEEE WIRELESS COMMUNICATIONS LETTERS 13 . 10 (2024) : 2842-2846 . |
| APA | Hu, Jinsong , Ye, Longjie , Chen, Youjia , Zhang, Xuefei , Wang, Jun , Chen, Zhizhang . Covert Communications for Text Semantic With Finite Blocklength . | IEEE WIRELESS COMMUNICATIONS LETTERS , 2024 , 13 (10) , 2842-2846 . |
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