Online DRL-based beam selection for RIS-aided physical layer security: an experimental study

Nasser, Ahmed, Celik, Abdulkadir, Abdallah, Asmaa, Cachón, David Lago, Wang, Ruiqi, Yang, Yiming, Shamim, Atif and Eltawil, Ahmed M. (2024) Online DRL-based beam selection for RIS-aided physical layer security: an experimental study. In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE. pp. 1004-1009 . (doi:10.1109/GLOBECOM52923.2024.10901057).

Abstract

The integration of reconfigurable intelligent surfaces (RIS) and artificial noise (AN) significantly enhances physical layer security (PLS) in wireless networks, provided that RIS's phase shifts are precisely optimized to prevent security vulnerabilities. This paper introduces a reinforcement learning (RL)based algorithm designed to optimize the phase shifts in RIS-partitioning-aided PLS systems operating in the millimeter wave (mm-Wave), without requiring channel state information (CSI) for any users. The RL algorithm optimizes the phase shifts by efficiently selecting the best beam from a predefined codebook for different partitions, which simultaneously enhances the intended signal for legitimate users and increases the effectiveness of AN on eavesdroppers, thereby maximizing the system's secrecy capacity (SC) and addressing the inherent non-convex challenges. Additionally, the paper details the development of an experimental testbed that provides essential data to refine the algorithm. The numerical results from the testbed highlight the significant impact of RIS partitioning in PLS, which can enhance the SC by an average of 55% over the full RIS scenario, and confirm the effectiveness of the RL-based algorithm in reducing computational complexity by approximately 80% compared to the exhaustive search algorithm.

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Contributors

Author: Abdulkadir Celik

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