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学者姓名：王俊
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With the increasing demand of wireless communication, the data rates supported by wireless communication technology are required to be higher, which also leads to the shortage of spectrum resources. Cognitive radio (CR) is considered a promising technology that can make full use of spectrum resources. Currently, Intelligent Reflecting Surfaces (IRSs) are gaining popularity. They autonomously regulate the wireless communication environment to achieve higher data rates and better coverage. In this paper, an IRS-based model is proposed to solve the energy efficiency (EE) optimization problem, which is in multiuser multiple input single output (MISO) energy harvesting CR (EH-CR) communication systems. The model enables the secondary transmitter (ST) to utilize EH technology to capture radio frequency (RF) energy from the primary user (PU), thereby powering its own data transmission. Due to the complexity and continuity of action and state space, a softmax deep double deterministic policy gradients method based on prioritized experience replay (PER-SD3) is designed to jointly optimize the transmission beamforming vector of the ST and the IRS reflection phase shift matrix. Simulation results show that the proposed method can improve the EE of underlay EH-CR communication systems by 35.3% at most compared with the benchmark cases. © 2015 IEEE.
Keyword ：
cognitive radio cognitive radio deep reinforcement learning deep reinforcement learning energy efficiency optimization energy efficiency optimization Energy harvesting Energy harvesting intelligent reflecting surface intelligent reflecting surface
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| GB/T 7714 | Wang, J. , Pei, H. , Yang, L. et al. Energy Efficiency Optimization of IRS-Aided Multiuser MISO Underlay EH-CR Communication Systems By Using PER-SD3 Method [J]. | IEEE Transactions on Cognitive Communications and Networking , 2025 . |
| MLA | Wang, J. et al. "Energy Efficiency Optimization of IRS-Aided Multiuser MISO Underlay EH-CR Communication Systems By Using PER-SD3 Method" . | IEEE Transactions on Cognitive Communications and Networking (2025) . |
| APA | Wang, J. , Pei, H. , Yang, L. , Hu, J. , Wu, L. , Shu, F. . Energy Efficiency Optimization of IRS-Aided Multiuser MISO Underlay EH-CR Communication Systems By Using PER-SD3 Method . | IEEE Transactions on Cognitive Communications and Networking , 2025 . |
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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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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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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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In the Energy-Harvesting (EH) Cognitive Internet of Things (EH-CIoT) network, due to the broadcast nature of wireless communication, the EH-CIoT network is susceptible to jamming attacks, which leads to a serious decrease in throughput. Therefore, this paper investigates an anti-jamming resource-allocation method, aiming to maximize the Long-Term Throughput (LTT) of the EH-CIoT network. Specifically, the resource-allocation problem is modeled as a Markov Decision Process (MDP) without prior knowledge. On this basis, this paper carefully designs a two-dimensional reward function that includes throughput and energy rewards. On the one hand, the Agent Base Station (ABS) intuitively evaluates the effectiveness of its actions through throughput rewards to maximize the LTT. On the other hand, considering the EH characteristics and battery capacity limitations, this paper proposes energy rewards to guide the ABS to reasonably allocate channels for Secondary Users (SUs) with insufficient power to harvest more energy for transmission, which can indirectly improve the LTT. In the case where the activity states of Primary Users (PUs), channel information and the jamming strategies of the jammer are not available in advance, this paper proposes a Linearly Weighted Deep Deterministic Policy Gradient (LWDDPG) algorithm to maximize the LTT. The LWDDPG is extended from DDPG to adapt to the design of the two-dimensional reward function, which enables the ABS to reasonably allocate transmission channels, continuous power and work modes to the SUs, and to let the SUs not only transmit on unjammed channels, but also harvest more RF energy to supplement the battery power. Finally, the simulation results demonstrate the validity and superiority of the proposed method compared with traditional methods under multiple jamming attacks.
Keyword ：
anti-jamming method anti-jamming method EH-CIoT network EH-CIoT network linearly weighted deep deterministic policy gradient linearly weighted deep deterministic policy gradient resource allocation resource allocation
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| GB/T 7714 | Li, Fushuai , Bao, Jiawang , Wang, Jun et al. Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm [J]. | SENSORS , 2024 , 24 (16) . |
| MLA | Li, Fushuai et al. "Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm" . | SENSORS 24 . 16 (2024) . |
| APA | Li, Fushuai , Bao, Jiawang , Wang, Jun , Liu, Da , Chen, Wencheng , Lin, Ruiquan . Anti-Jamming Resource-Allocation Method in the EH-CIoT Network through LWDDPG Algorithm . | SENSORS , 2024 , 24 (16) . |
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In Cognitive Radio (CR) networks combined with Energy Harvesting (EH) technology, Secondary Users (SUs) are vulnerable to jamming attacks when sensing idle channels. At the same time, they may encounter numerous jamming and eavesdropping attacks during the data transmission phase. This paper examines the scenario in which SUs are susceptible to malicious attacks and energy constraints in both the sensing and transmission phases. We propose a utility function applicable to a single time slot. The blockchain uses Smart Contract (SC) technology to set rewards and punishments for users' channel selection behavior and adjust mining difficulty. This method combines blockchain with spectrum sensing data fusion, abandons the decision-making mechanism of the traditional Cooperative Spectrum Sensing (CSS) Fusion Center (FC), and adopts a distributed structure to ensure the security and reliability of sensing data fusion. In addition, this paper uses the potential game and the Stackelberg game to study the optimal transmission channel and optimal time slot allocation strategy for SUs under malicious attacks. Considering the possible interference caused by channel switching and the greedy principle of Malicious User (MU), the proposed two-layer game method gradually optimizes the sensing detection probability and secure communication rate with time slot iteration. In order to further improve the secure communication rate, an iterative update formula for transmission power is given to make reasonable use of the remaining energy of each SU at the end of each time slot. Simulation results show that the proposed method is superior to traditional methods in both sensing performance and secure communication rate.
Keyword ：
blockchain blockchain Blockchains Blockchains Eavesdropping Eavesdropping eavesdropping attack eavesdropping attack Energy harvesting Energy harvesting Full-duplex system Full-duplex system Games Games Jamming Jamming jamming attack jamming attack Power system reliability Power system reliability Probability Probability Security Security Sensors Sensors Throughput Throughput two-layer game two-layer game
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| GB/T 7714 | Hou, Haifeng , Lin, Ruiquan , Wang, Jun et al. Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks [J]. | IEEE ACCESS , 2024 , 12 : 146996-147011 . |
| MLA | Hou, Haifeng et al. "Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks" . | IEEE ACCESS 12 (2024) : 146996-147011 . |
| APA | Hou, Haifeng , Lin, Ruiquan , Wang, Jun , Li, Sheng , Chen, Wencheng . Blockchain and Game Theory-Based Strategies for Anti-Jamming and Eavesdropping in EH-CR Networks . | IEEE ACCESS , 2024 , 12 , 146996-147011 . |
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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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This article proposes a novel graph recurrent neural network (GRNN)-based approach for detecting the eavesdropping attacks in smart grid wireless communication systems enabled by simultaneous wireless information and power transfer (SWIPT). By leveraging the graph-centric nature of GRNNs, the proposed method effectively learns the topological structure and the edge features of the wireless sensor networks (WSNs), enabling the detection of the eavesdropping attacks in dynamic WSNs. This article mathematically models the channel state information (CSI) under the man-in-the-middle eavesdropping attacks based on the physical-layer security (PLS) in SWIPT networks. Moreover, this article sets up a real-world testbed to create the training and testing data sets. The proposed GRNN model can handle large-scale complex topologies and dynamic eavesdropping networks, accurately detect eavesdropping behaviors, and enhance the security of information transmission in WSNs. Simulation results demonstrate that, compared with the algorithms, such as support vector machine (SVM), K-nearest neighbors (KNNs), convolutional neural network (CNN), graph convolutional network (GCN), and gated recurrent unit (GRU), the proposed method exhibits stronger robustness under complex attack scenarios, achieving a detection accuracy of over 95%. This article provides a novel and effective graph learning solution for the smart grid wireless communication security, which is of great significance to ensure the stable and reliable operation of the smart grids.
Keyword ：
Eavesdropping attacks Eavesdropping attacks graph recurrent neural network (GRNN) graph recurrent neural network (GRNN) secure communications secure communications smart grid smart grid
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| GB/T 7714 | Jiang, Weibin , Wang, Jun , Hsiung, Kan-Lin et al. GRNN-Based Detection of Eavesdropping Attacks in SWIPT-Enabled Smart Grid Wireless Sensor Networks [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (22) : 37381-37393 . |
| MLA | Jiang, Weibin et al. "GRNN-Based Detection of Eavesdropping Attacks in SWIPT-Enabled Smart Grid Wireless Sensor Networks" . | IEEE INTERNET OF THINGS JOURNAL 11 . 22 (2024) : 37381-37393 . |
| APA | Jiang, Weibin , Wang, Jun , Hsiung, Kan-Lin , Chen, Hsin-Yu . GRNN-Based Detection of Eavesdropping Attacks in SWIPT-Enabled Smart Grid Wireless Sensor Networks . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (22) , 37381-37393 . |
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Rapid identification of peanut seed quality is crucial for public health. In this study, we present a terahertz wave imaging system using a convolutional neural network (CNN) machine learning approach. Terahertz waves are capable of penetrating the seed shell to identify the quality of peanuts without causing any damage to the seeds. The specificity of seed quality on terahertz wave images is investigated, and the image characteristics of five different qualities are summarized. Terahertz wave images are digitized and used for training and testing of convolutional neural networks, resulting in a high model accuracy of 98.7% in quality identification. The trained THz-CNNs system can accurately identify standard, mildewed, defective, dried and germinated seeds, with an average detection time of 2.2 s. This process does not require any sample preparation steps such as concentration or culture. Our method swiftly and accurately assesses shelled seed quality non-destructively.
Keyword ：
Aflatoxin Aflatoxin Machine learning Machine learning Peanut Peanut Quality identification Quality identification Terahertz imaging technology Terahertz imaging technology
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| GB/T 7714 | Jiang, Weibin , Wang, Jun , Lin, Ruiquan et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut [J]. | FOOD CHEMISTRY-X , 2024 , 23 . |
| MLA | Jiang, Weibin et al. "Machine learning-based non-destructive terahertz detection of seed quality in peanut" . | FOOD CHEMISTRY-X 23 (2024) . |
| APA | Jiang, Weibin , Wang, Jun , Lin, Ruiquan , Chen, Riqing , Chen, Wencheng , Xie, Xin et al. Machine learning-based non-destructive terahertz detection of seed quality in peanut . | FOOD CHEMISTRY-X , 2024 , 23 . |
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