Deep Reinforcement Learning-Based Joint Optimization for Physical Layer Security in IRS-Assisted High-Speed Railway Networks

With the increasing demand for data security in high-speed railway (HSR) communications, physical layer security (PLS) has emerged as an effective approach to enhance the secrecy performance of wireless systems. This paper investigates a millimeter-wave secure communication system for HSR scenarios, assisted by intelligent reflecting surface (IRS), where the channel state information (CSI) of the eavesdropper (Eve) is unavailable. The primary objective is to minimize the secrecy outage probability (SOP). First, a closed-form expression of SOP with imperfect CSI is derived based on a statistical channel model, which provides a theoretical foundation for performance evaluation. Then, to address the difficulty of channel prediction in high-mobility scenarios, an attention-enhanced long short-term memory (Attention-LSTM) model is proposed to predict the CSI of the mobile relay (MR), effectively mitigating the channel aging issue caused by CSI latency. To tackle the issues of non-convex optimization and tightly coupled variables in the joint design of transmit beamforming and IRS phase adjustments, a twin delayed deep deterministic policy gradient (TD3) algorithm is employed to derive an effective secrecy enhancement strategy for HSR communications. Simulation outcomes indicate that the proposed framework achieves superior secrecy performance compared to conventional optimization methods, highlighting its robustness and practical applicability.