DRL-Based IRS-Assisted Secure Visible Light Communications

Abstract

In this paper, we develop a novel Physical Layer Security (PLS) technique for a Visible Light Communication (VLC) system composed of light fixtures assisted by mirror array sheets serving as Intelligent Reflecting Surfaces (IRS). Our objective is to optimize the Secrecy Capacity (SC) by finding the optimal beamforming (BF) weights equipped at the VLC fixtures and mirror orientations at the mirror array sheet. Due to many design parameters, including the beamforming weights, the mirror orientations, and the mobility of the users, conventional optimization techniques may not be practical to optimize the SC capacity. Therefore, we proposed a Deep Reinforcement Learning (DRL) solution based on Deep Deterministic Policy Gradient (DDPG) algorithm to solve the highly complex SC problem by adjusting the BF weights and mirror orientations. The DDPG-based algorithm provides an optimized solution that can adapt to the large size of design parameters and act fast to the channel variations due to users’ mobility. Our results show that considering both mirror array sheet and BF vectors provide the highest SC for the system. Moreover, we show the effect of changing the mirror arrangements of the mirror array sheet on SC. We conclude that for a fixed mirror array sheet size, there exists a specific mirror arrangement (i.e., number of mirrors) that optimizes the SC. After this number, the performance of SC saturates. We also show the trade-off between the training complexity and SC performance considering different mirror arrangements in the mirror array sheet.