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学者姓名：王俊
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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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Cognitive Radio (CR) and Energy Harvesting (EH) techniques have offered insights to mitigate issues related to inefficient spectrum utilization and limited energy storage capacity. In Cognitive Radio Networks, security threats, particularly from eavesdroppers, may result in information leakage. This study focuses on enhancing the Physical Layer Security (PLS) of multi-users with EH by employing cooperative jamming via a Autonomous Aerial Vehicle (AAV) to maximize the secure communication rate. In the AAV-assisted EH-CR system, Secondary Users (SUs) can utilize the licensed spectrum band occupied by a Primary User (PU) if the cooperative jamming power from SUs to the PU remains below a certain threshold. SUs can harvest and use Radio Frequency (RF) energy from the Primary Transmitter (PT). The AAV jammer disrupts the eavesdropper by transmitting jamming signals, thereby minimizing stolen information to optimize long-term secure communication performance. The paper formulates the problem of maximizing the average secure communication rate while considering system constraints and jointly optimizes the AAV trajectory, transmission power, and EH coefficient. As the problem is non-convex, it is reformulated as a Markov Decision Process (MDP). The paper employs the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm to address the problem, introduces counterfactual baselines to tackle the credit assignment problem in centralized learning, and integrates the Long Short-Term Memory (LSTM) network to enhance the learning capability of sequential sample data, thereby improving the training efficiency and effectiveness of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method in maximizing the system's secure communication rate.
Keyword ：
autonomous aerial vehicle (AAV) autonomous aerial vehicle (AAV) Autonomous aerial vehicles Autonomous aerial vehicles Cognitive radio (CR) Cognitive radio (CR) Communication system security Communication system security cooperative jamming cooperative jamming energy harvesting (EH) energy harvesting (EH) Interference Interference Jamming Jamming Optimization Optimization physical layer security (PLS) physical layer security (PLS) Radio frequency Radio frequency Relays Relays Resource management Resource management Security Security Trajectory Trajectory
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| GB/T 7714 | Wang, Jun , Wang, Rong , Zheng, Zibin et al. Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) : 4713-4727 . |
| MLA | Wang, Jun et al. "Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 74 . 3 (2025) : 4713-4727 . |
| APA | Wang, Jun , Wang, Rong , Zheng, Zibin , Lin, Ruiquan , Wu, Liang , Shu, Feng . Physical Layer Security Enhancement in AAV-Assisted Cooperative Jamming for Cognitive Radio Networks: A MAPPO-LSTM Deep Reinforcement Learning Approach . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2025 , 74 (3) , 4713-4727 . |
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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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Cognitive Internet of Vehicles (CIoV) adds the cognitive engine based on traditional Internet of Vehicles (IoV), which can improve spectrum utilization. However, spectrum sensing data falsification (SSDF) attacks pose a threat to CIoV network security. To ensure the full utilization of spectrum resources and protect primary users transmission, this article combines blockchain with CIoV to defend against SSDF attacks in the presence of vehicle users (VUs) entering and leaving the network. Specifically, this article introduces a virtual currency called Sencoins serve as credential for VUs to purchase transmission shares. And this article proposes a reward and punishment mechanism and a hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) mining model to thwart the motivation of the VUs to launch SSDF attacks. On this basis, this article investigates the dynamics of SSDF attack strategy choice of VUs, and uses the largest Lyapunov exponent (LLE) to determine the critical value of Sencoins that avoids the system to exhibit chaotic behavior. To describe the uncertainty of the population proportion of VUs that choose different attack strategies due to high-speed movement and the VUs entering and leaving the CIoV network, this article introduces Gaussian white noise into the replication dynamics equation and builds the It & ocirc; stochastic evolutionary game model, and solves it according to the stability judgment theorem of stochastic differential equations and stochastic Taylor expansion. Finally, simulation results verify that the proposed method can quickly and effectively thwart SSDF attacks in the CIoV network. And compared with traditional methods, the proposed method can improve the efficiency of defending against SSDF attacks by 567% and the average throughput by 25%.
Keyword ：
Blockchain Blockchain Blockchains Blockchains Cognitive Internet of Vehicles (CIoV) Cognitive Internet of Vehicles (CIoV) Data models Data models Games Games Interference Interference Internet of Vehicles Internet of Vehicles Security Security Sensors Sensors spectrum sensing data falsification (SSDF) attack spectrum sensing data falsification (SSDF) attack stochastic evolutionary game stochastic evolutionary game Stochastic processes Stochastic processes Throughput Throughput Wireless sensor networks Wireless sensor networks
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| GB/T 7714 | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng et al. Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game [J]. | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) : 2233-2250 . |
| MLA | Li, Fushuai et al. "Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game" . | IEEE INTERNET OF THINGS JOURNAL 12 . 2 (2025) : 2233-2250 . |
| APA | Li, Fushuai , Lin, Ruiquan , Chen, Wencheng , Wang, Jun , Shu, Feng , Chen, Riqing . Thwarting SSDF Attacks From High-Speed Movement VUs in the CIoV Network: Based on Blockchain and Stochastic Evolutionary Game . | IEEE INTERNET OF THINGS JOURNAL , 2025 , 12 (2) , 2233-2250 . |
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Cognitive radio (CR) is regarded as the key technology of the 6th-Generation (6G) wireless network. Because 6G CR networks are anticipated to offer worldwide coverage, increase cost efficiency, enhance spectrum utilization, and improve device intelligence and network safety. This article studies the secrecy communication in an energy-harvesting (EH)-enabled Cognitive Internet of Things (EH-CIoT) network with a cooperative jammer. The secondary transmitters (STs) and the jammer first harvest the energy from the received radio frequency (RF) signals in the EH phase. Then, in the subsequent wireless information transfer (WIT) phase, the STs transmit secrecy information to their intended receivers in the presence of eavesdroppers while the jammer sends the jamming signal to confuse the eavesdroppers. To evaluate the system secrecy performance, we derive the instantaneous secrecy rate and the closed-form expression of secrecy outage probability (SOP). Furthermore, we propose a deep reinforcement learning (DRL)-based framework for the joint EH time and transmission power allocation problems. Specifically, a pair of ST and jammer over each time block is modeled as an agent which is dynamically interacting with the environment by the state, action, and reward mechanisms. To better find the optimal solutions to the proposed problems, the long short-term memory (LSTM) network and the generative adversarial networks (GANs) are combined with the classical DRL algorithm. The simulation results show that our proposed method is highly effective in maximizing the secrecy rate while minimizing the SOP compared with other existing schemes.
Keyword ：
6G mobile communication 6G mobile communication Cognitive radio (CR) network Cognitive radio (CR) network Communication system security Communication system security deep reinforcement learning (DRL) deep reinforcement learning (DRL) energy harvesting (EH) energy harvesting (EH) Internet of Things Internet of Things Jamming Jamming Mobile handsets Mobile handsets physical-layer security (PLS) enhancement physical-layer security (PLS) enhancement Resource management Resource management Wireless communication Wireless communication
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| GB/T 7714 | Lin, Ruiquan , Qiu, Hangding , Wang, Jun et al. Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) : 4899-4913 . |
| MLA | Lin, Ruiquan et al. "Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 11 . 3 (2024) : 4899-4913 . |
| APA | Lin, Ruiquan , Qiu, Hangding , Wang, Jun , Zhang, Zaichen , Wu, Liang , Shu, Feng . Physical-Layer Security Enhancement in Energy-Harvesting-Based Cognitive Internet of Things: A GAN-Powered Deep Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) , 4899-4913 . |
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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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| GB/T 7714 | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin et al. Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) [J]. | PLOS ONE , 2024 , 19 (12) . |
| MLA | Liao, Jianbin et al. "Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023)" . | PLOS ONE 19 . 12 (2024) . |
| APA | Liao, Jianbin , Yu, Hongliang , Jiang, Weibin , Lin, Ruiquan , Wang, Jun . Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks (vol 18, e0279886, 2023) . | PLOS ONE , 2024 , 19 (12) . |
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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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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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This research proposes an innovative intelligent detection methodology tailored for the high-speed train catenary system, leveraging FPGA-accelerated MobileNetV2. Exploiting the exceptional computational capabilities of the MobileNetV2 convolutional neural network, the methodology incorporates Quantization Aware Training (QAT) to judiciously compress the comprehensive network parameters to one-fourth of the original configuration, ensuring judicious and efficient intelligent detection for the high-speed train catenary system. Notably, the entirety of network weights is strategically allocated to the on-chip resources of the FPGA, effectively circumventing constraints inherent to off-chip storage bandwidth. This strategic allocation addresses power consumption challenges linked to accessing off-chip storage resources, culminating in a substantial augmentation of the real-time operational efficiency of the network.The proposed system, an intricately tuned and energy-efficient Lightweight Convolutional Neural Network (MobileNetV2) recognition system, is meticulously implemented on the Xilinx Virtex-7 VC707 development board. Operating seamlessly at a clock frequency of 200Hz, the system attains an impressive throughput of 170.06 GOP/s with a mere power consumption of 6.13W. The resultant energy efficiency ratio excels at 27.74 GOP/s/W, significantly outpacing the CPU by a factor of 92 and the GPU by a factor of 25. These findings underscore substantial performance advantages when juxtaposed with alternative implementations. © 2024 ACM.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning Electric current collection Electric current collection Electric power utilization Electric power utilization Energy efficiency Energy efficiency Field programmable gate arrays (FPGA) Field programmable gate arrays (FPGA) Learning systems Learning systems Pantographs Pantographs Railroad cars Railroad cars Railroads Railroads
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| GB/T 7714 | Wang, Rong , Chen, Shenglan , Wang, Jun et al. Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph System Status Based on Deep learning [C] . 2024 . |
| MLA | Wang, Rong et al. "Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph System Status Based on Deep learning" . (2024) . |
| APA | Wang, Rong , Chen, Shenglan , Wang, Jun , Chen, Wenchen , Pei, Hai . Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph System Status Based on Deep learning . (2024) . |
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