Environment-Aware Beam Selection for RIS-Assisted Secure Wireless Communication with Channel Knowledge Map

Abstract

Reconfigurable intelligent surface (RIS)-assisted communication system is a promising technology for secure wireless communication. However, achieving the maximum communication rate requires perfect channel state information, which is particularly challenging in practical scenarios for RIS-assisted communication. Although various channel estimation methods have been proposed, they typically demand high training overhead, which increases with the number of RIS elements. To address this challenge, a novel method is proposed by using alternating optimization (AO) and channel knowledge map techniques. This method does not require real-time CSI, reducing the complexity of RIS-assisted communication in practical applications, and significantly lowering the training overhead while ensuring the secrecy rate. It innovatively uses a regional communication rate visualization map to analyze the secrecy rate while considering user location errors. Simulation results show that this proposed scheme surpasses those based on channel estimation, especially exhibiting strong robustness to errors related to terminal positioning in practical applications.