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<p>With the growing demand for secure and energy-efficient wireless communication in dynamic and energy-constrained environments, integrating unmanned aerial vehicle (UAV) with intelligent reflecting surface (IRS) has emerged as a promising solution. However, air–ground communication still faces critical challenges such as eavesdropping threats and limited onboard energy of UAVs. To address these issues, this paper proposes a physical layer security (PLS) transmission framework for UAV-IRS-assisted communication systems. The proposed scheme incorporates artificial noise (AN) and simultaneous wireless information and power transfer (SWIPT) to enhance secrecy performance and ensure sustained energy harvesting (EH). The system jointly optimizes the base station (BS) beamforming, UAV positioning, and IRS phase shift to maximize the secrecy rate (SR) under EH constraints. To solve the resulting non-convex optimization problem, we design a deep reinforcement learning (DRL)-based approach using the twin delayed deep deterministic policy gradient (TD3) algorithm. Simulation results demonstrate that the proposed method significantly improves both secrecy and energy efficiency compared to existing baseline schemes.</p>

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Secure and energy-efficient transmission in UAV-assisted intelligent reflecting surface networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Jianbin Xue,
  • Jialing Xu,
  • Xiangrui Guan,
  • Han Zhang,
  • Yourong Gan

摘要

With the growing demand for secure and energy-efficient wireless communication in dynamic and energy-constrained environments, integrating unmanned aerial vehicle (UAV) with intelligent reflecting surface (IRS) has emerged as a promising solution. However, air–ground communication still faces critical challenges such as eavesdropping threats and limited onboard energy of UAVs. To address these issues, this paper proposes a physical layer security (PLS) transmission framework for UAV-IRS-assisted communication systems. The proposed scheme incorporates artificial noise (AN) and simultaneous wireless information and power transfer (SWIPT) to enhance secrecy performance and ensure sustained energy harvesting (EH). The system jointly optimizes the base station (BS) beamforming, UAV positioning, and IRS phase shift to maximize the secrecy rate (SR) under EH constraints. To solve the resulting non-convex optimization problem, we design a deep reinforcement learning (DRL)-based approach using the twin delayed deep deterministic policy gradient (TD3) algorithm. Simulation results demonstrate that the proposed method significantly improves both secrecy and energy efficiency compared to existing baseline schemes.