Novel Reconfigurable Intelligent Surfaces-Aided Schemes for Physical Layer Security Enhancement of IoT Networks

The telecommunications sector has seen a significant surge in innovative advancements, particularly in wireless communication, driven by increasing connectivity demands. With wireless networks and Internet of Things (IoT) devices now pervasive across civilian, military, and medical sectors, security has become an imperative, constrained by the low power of IoT devices. In response, physical layer security (PLS) stood out as a tool for establishing keyless, secure communication, leveraging the physical layer parameters and other wireless communication techniques such as precoding, beamforming, and reconfigurable intelligent surfaces (RIS). RIS gained popularity for its efficiency in reflecting the legitimate signal with high power and low power for the illegitimate one. Yet, the optimal method of configuring its phase shift continues to require investigation.

This work proposes three RIS phase shift configuration schemes in a RIS-and-jamming-aided network. The network consists of a transmitter and a multi-antenna receiver under the threat of several independent eavesdroppers. The proposed configuration schemes are a deep reinforcement learning-based (DRL-based) technique and two online model based configuration schemes. Numerical results simulating these configuration schemes and evaluating their performance are provided. Finally, the three methods are compared and assessed based on PLS analysis metric.