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<p>In this paper, we investigate a secure integrated sensing and communication (ISAC) system aided by a reconfigurable intelligent surface (RIS), wherein a base station (BS) jointly delivers the communication services to legitimate users (LU) and performs radar target detection through unified signal transmission. The deployment of a RIS in the ISAC system enables the creation of virtual line-of-sight (LOS) links for communication and detection to overcome blockages and enhance security. We formulate an optimization problem to maximize the signal-to-noise ratio (SNR) of the radar output by jointly designing the beamforming in the BS and the RIS reflection coefficient while satisfying several constraints of transmit power budget, communication performance requirement, and secure transmission requirement. However, due to the dynamics and complexity of the system, traditional convex optimization methods are extremely computationally intensive. Thus, we adopt deep reinforcement learning (DRL)-based algorithms. Simulation results verify the deployment of RIS enhances system performance and demonstrate the DRL-based algorithms outperform the traditional convex optimization algorithms.</p>

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DRL-Based Joint Beamforming and Reflection Design for Secure RIS-Aided ISAC Systems

  • 期刊论文

  • 发表日期：

    2025年10月29日

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  • Hongqiao Liu,
  • Lina Zheng,
  • Chao Zhai,
  • Wei Ji,
  • Jieyi Yan,
  • Yawen Zhang

摘要

In this paper, we investigate a secure integrated sensing and communication (ISAC) system aided by a reconfigurable intelligent surface (RIS), wherein a base station (BS) jointly delivers the communication services to legitimate users (LU) and performs radar target detection through unified signal transmission. The deployment of a RIS in the ISAC system enables the creation of virtual line-of-sight (LOS) links for communication and detection to overcome blockages and enhance security. We formulate an optimization problem to maximize the signal-to-noise ratio (SNR) of the radar output by jointly designing the beamforming in the BS and the RIS reflection coefficient while satisfying several constraints of transmit power budget, communication performance requirement, and secure transmission requirement. However, due to the dynamics and complexity of the system, traditional convex optimization methods are extremely computationally intensive. Thus, we adopt deep reinforcement learning (DRL)-based algorithms. Simulation results verify the deployment of RIS enhances system performance and demonstrate the DRL-based algorithms outperform the traditional convex optimization algorithms.