Adaptive DRL for IRS Mirror Orientation in Dynamic OWC Networks

Abstract

Intelligent reflecting surfaces (IRSs) have emerged as a promising solution to mitigate line-of-sight (LoS) blockages and enhance signal coverage in optical wireless communication (OWC) systems. In this work, we consider a mirror-based IRS to assist a dynamic indoor visible light communication (VLC) environment. We formulate an optimization problem that aims to maximize the sum rate by adjusting the orientation of the IRS mirrors. To enable real-time adaptability, the problem is modelled as a Markov decision process (MDP), and a deep reinforcement learning (DRL) algorithm, specifically deep deterministic policy gradient (DDPG), is employed to optimize mirror orientation toward mobile users under blockage and mobility constraints. Simulation results demonstrate that the proposed DDPG-based approach outperforms conventional DRL algorithms and achieves substantial improvements in sum rate compared to fixed-orientation IRS configurations.

Tasks

Reproductions