Scholarlink

In this paper, a reconfigurable intelligent surface mounted on an unmanned aerial vehicle (UAV-Mounted RIS) assisted integrated sensing and communication (ISAC) secure transmission system is investigated, in which the sensing target (ST) is also regarded as an illegal eavesdropper. Specifically, the airborne RIS is utilized as the relay platform, While introducing solid and dependable line-of-sight (LoS ) links. Our objective is to optimize the aggregate secure communication rate for legitimate users, ensuring compliance with the minimum perceived echo signal-to-noise-ratio requirement, while concurrently addressing the challenges posed by multiple eavesdroppers. To achieve this purpose, the active beamforming and the deployment of UAV in 3D-space together with the phase shift matrix are jointly optimized. The proposed optimization problem is nonconvex on account of the complex coupling between multiple variables in the channel state information (CSI). To address this intractable challenge, we propose a deep reinforcement learning (DRL) framework based on the soft-actor-critic (SAC) algorithm. Simulation results verify the feasibility and efficacy of our proposed scheme.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep Reinforcement Learning-Based Secure Transmission for UAV-Mounted RIS Aided ISAC Systems

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Gangcan Sun,
  • Kaihao Wang,
  • Zhengyu Zhu,
  • Zheng Chu,
  • Zheng Li

摘要

In this paper, a reconfigurable intelligent surface mounted on an unmanned aerial vehicle (UAV-Mounted RIS) assisted integrated sensing and communication (ISAC) secure transmission system is investigated, in which the sensing target (ST) is also regarded as an illegal eavesdropper. Specifically, the airborne RIS is utilized as the relay platform, While introducing solid and dependable line-of-sight (LoS ) links. Our objective is to optimize the aggregate secure communication rate for legitimate users, ensuring compliance with the minimum perceived echo signal-to-noise-ratio requirement, while concurrently addressing the challenges posed by multiple eavesdroppers. To achieve this purpose, the active beamforming and the deployment of UAV in 3D-space together with the phase shift matrix are jointly optimized. The proposed optimization problem is nonconvex on account of the complex coupling between multiple variables in the channel state information (CSI). To address this intractable challenge, we propose a deep reinforcement learning (DRL) framework based on the soft-actor-critic (SAC) algorithm. Simulation results verify the feasibility and efficacy of our proposed scheme.