DRL-Enhanced Security in RIS-Assisted Systems Simultaneous Against Eavesdropping and Jamming

The escalating demand for secure wireless communication, driven by the proliferation of connected devices, has made information security a paramount concern. Traditional encryption methods struggle against eavesdropping and malicious jamming in open wireless channels, necessitating innovative solutions. This paper explores physical layer security, leveraging intrinsic wireless channel properties, enhanced by reconfigurable intelligent surface (RIS). We propose an RIS-assisted multiple-input single-output system to counteract eavesdroppers and jamming threats through an artificial noise (AN) cooperation scheme. AN is injected during legitimate beamforming to bolster transmission security. Jointly optimizing the base station’s precoding vector, AN vector, and RIS reflection coefficients under power, quality, and phase constraints, we formulate a non-convex secrecy rate (SR) maximization problem. Addressing its complexity, we model it as a Markov decision process and develop a deep deterministic policy gradient algorithm within a deep reinforcement learning framework. Simulation results demonstrate the proposed method’s superior performance, reduced complexity, and robustness against multiple eavesdroppers and high-power jamming, underscoring the critical roles of RIS and AN in securing wireless communications. It consistently outperforms traditional algorithms such as alternating optimization with semidefinite programming, offering up to 7% higher SR and achieving nearly 120% SR gains as RIS elements scale from N =4 to N =10.