A Robust Reconfigurable Intelligent Surface-Aided Physical Layer Authentication Scheme

In this paper, a robust reconfigurable intelligent surface (RIS)-aided carrier frequency offset (CFO)-based physical layer authentication (PLA) scheme for wireless networks is proposed. The considered network consists of a legitimate transmitter, a spoofer, and a receiver, acting as an authenticator, who aims to identify the sender's legitimacy relying on the estimated CFO from received signals. Thus, the proposed scheme exploits an RIS to increase the received signal-to-noise ratio (SNR) and enhance the authentication performance. A deep reinforcement learning framework is developed to jointly optimize the RIS phase shifts and the preamble length to maximize the authentication performance under a minimal channel capacity constraint. Then, a supervised machine learning classifier is employed for node authentication, exploiting the optimized RIS reflection and preamble length. The results show that the authentication performance is enhanced with the increase in the RIS size and the difference between the transmitters' CFOs. Also, the proposed scheme outperforms the baseline RIS-aided CSI-based one in mobility scenarios.