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学者姓名：王俊
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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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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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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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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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With the rapid development of high-speed railway technology, ensuring its operational safety has become an important issue. In particular, real-time monitoring of the high-speed railway pantograph network system is of great significance for preventing failures and reducing accidents. This research aims to improve the intelligent detection performance of high-speed railway pantograph network status through the improved YOLO algorithm. Research methods include the use of deep learning technology and image processing technology, focusing on improving the YOLO algorithm to enhance its detection accuracy in complex environments, especially its ability to identify small targets and its adaptability to dynamic environments. It is expected that through these improvements, more accurate and efficient status monitoring of high-speed railway pantographs will be achieved, thereby improving the safe operation level of high-speed railways. © 2024 IEEE.
Keyword ：
Deep learning Deep learning Electric current collection Electric current collection Engineering education Engineering education Image enhancement Image enhancement Learning algorithms Learning algorithms Pantographs Pantographs Railroad accidents Railroad accidents 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 Network Status Based on Improved YOLO Algorithm [C] . 2024 : 353-357 . |
| MLA | Wang, Rong et al. "Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph Network Status Based on Improved YOLO Algorithm" . (2024) : 353-357 . |
| APA | Wang, Rong , Chen, Shenglan , Wang, Jun , Chen, Wenchen , Pei, Hai . Research on Key Technologies for Intelligent Detection of High-Speed Railway Pantograph Network Status Based on Improved YOLO Algorithm . (2024) : 353-357 . |
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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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Cognitive Industrial Internet of Things (CIIoT) permits Secondary Users (SUs) to use the spectrum bands owned by Primary Users (PUs) opportunistically. However, in the absence of the PUs, the selfish SUs could mislead the normal SUs to leave the spectrum bands by initiating a Primary User Emulation Attack (PUEA). In addition, the application of Energy Harvesting (EH) technology can exacerbate the threat of security. Because the energy cost of initiating a PUEA is offset to some extent by EH technology which can proactively replenish the energy of the selfish nodes. Thus, EH technology can increase the motivation of the selfish SUs to initiate a PUEA. To address the higher motivation of the selfish SUs attacking in CIIoT scenario where the EH technology is applied, in this paper, an EH-PUEA system model is first established to study the security countermeasures in this severe scenario of PUEA problems. Next, a new reward and punishment defense management mechanism is proposed, and then the dynamics of the selfish SUs and the normal SUs in a CIIoT network are studied based on Evolutionary Game Theory (EGT), and the punishment parameter is adjusted according to the dynamics of the selfish SUs to reduce the proportion of the selfish SUs' group choosing an attack strategy, so as to increase the throughput achieved by the normal SUs' group. Finally, the simulation results show that the proposed mechanism is superior to the conventional mechanism in terms of throughput achieved by the normal SUs' group in CIIoT scenario with EH technology applied.
Keyword ：
Cognitive industrial Internet of Things (CIIoT) Cognitive industrial Internet of Things (CIIoT) Energy harvesting Energy harvesting energy harvesting (EH) energy harvesting (EH) evolutionary game theory (EGT) evolutionary game theory (EGT) Games Games Game theory Game theory Industrial Internet of Things Industrial Internet of Things Jamming Jamming primary user emulation attack (PUEA) primary user emulation attack (PUEA) Security Security Throughput Throughput
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| GB/T 7714 | Wang, Jun , Pei, Hai , Wang, Ruiliang et al. Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (4) : 4777-4786 . |
| MLA | Wang, Jun et al. "Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 21 . 4 (2024) : 4777-4786 . |
| APA | Wang, Jun , Pei, Hai , Wang, Ruiliang , Lin, Ruiquan , Fang, Zhou , Shu, Feng . Defense Management Mechanism for Primary User Emulation Attack Based on Evolutionary Game in Energy Harvesting Cognitive Industrial Internet of Things . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (4) , 4777-4786 . |
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