Volume 14 Issue 4
Aug.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(4): 1005-1018
WEI Zhiqiang, ZHANG Jiashuo, LIU Fan, et al. Low-altitude secure communication driven by deep reinforcement learning: An integrated sensing and communication design[J]. Journal of Radars, 2025, 14(4): 1005–1018. doi: 10.12000/JR25025
Citation: WEI Zhiqiang, ZHANG Jiashuo, LIU Fan, et al. Low-altitude secure communication driven by deep reinforcement learning: An integrated sensing and communication design[J]. Journal of Radars, 2025, 14(4): 1005–1018. doi: 10.12000/JR25025
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Low-altitude Secure Communication Driven by Deep Reinforcement Learning: An Integrated Sensing and Communication Design

DOI: 10.12000/JR25025 CSTR: 32380.14.JR25025
  • 1.

    Xi’an Jiaotong University, Xi’an 710049, China

  • 2.

    Southeast University, Nanjing 211189, China

  • 3.

    Beijing Institute of Technology, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (12371464, 62331023, 62471039), Qin Chuang Yuan High-Level Innovation and Entrepreneurship Talent Program (QCYRCXM-2023-094), Guangdong Basic and Applied Basic Research Foundation (2024A1515011218)
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  • Corresponding author: YANG Zai, yangzai@xjtu.edu.cn
  • Received Date: 2025-02-04
  • Rev Recd Date: 2025-06-30
    • Available Online: 2025-07-02
  • Publish Date: 2025-07-09
    Abstract
  • To address the physical layer security challenges in low-altitude Unmanned Aerial Vehicle (UAV) communications, this paper proposes an Integrated Sensing And Communication (ISAC) scheme. For the proposed ISAC scheme, an online optimization framework for UAV trajectory and communication resource allocation is developed using Deep Reinforcement Learning (DRL). In the proposed scheme, artificial noise transmitted by a communication UAV is reused to simultaneously sense and jam a potential eavesdropping UAV, thereby enhancing secure communications for ground users. By estimating and predicting the state of the eavesdropping UAV, the trajectory and resource allocation design problem is reformulated as a Markov decision process. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, the optimal framework is learned over time, dynamically optimizing the communication UAV’s trajectory and resource allocation to maximize long-term sensing and secure communication performance. Simulation results demonstrate that the proposed scheme achieves a superior trade-off between sensing and security without degrading sensing performance and outperforms baseline methods in terms of secure communication performance. This validates the performance gains achieved through sensing and online trajectory design, as well as the potential and superior performance of applying DRL to the integrated design of sensing, communication, and trajectory.

     

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