Secure transmission for reconfigurable intelligent surface assisted communication with CV‐DDPG

Abstract

Abstract Reconfigurable intelligent surface (RIS) has great potential in securing wireless transmission, and it can flexibly change the wireless communication environment. However, the employment of RIS increases the difficulty in beamforming of base station (BS), and existing schemes cannot be directly utilized to enhance the security of system. Therefore, a reinforcement learning framework to jointly control BS and RIS for the purpose of enhancing the secure performance is proposed. Specifically, the beamforming ia transformed with artificial noise and reflection control as a Markov decision problem (MDP) in complex domain. Then, we develop the deep deterministic policy gradient based on complex‐valued neural networks (CV‐DDPG) to imitate the rules of complex computation. Additionally, the CV‐DDPG is performed as the agent of reinforcement learning to control the BS and RIS, which enables better utilization of the implied phase information of complex values in the system. The simulation results show that the proposed model promotes the performance of the control and greatly improves the secrecy rate of the legitimate user.

Keywords

• beam steering
• learning (artificial intelligence)
• multiple‐input‐single‐output communication
• telecommunication security
• telecommunication control