S-IRIS: Statistical CSI based Optimization for Irregular RIS using Reinforcement Learning

In this paper, we investigate the transmission design for irregular reconfigurable intelligent surface (IRIS) downlink communications in 6G multiuser wireless systems. We formulate it as an optimization problem based on the statistical channel state information (CSI), where we jointly optimize the precoding at the base station (BS), and the antenna element selection and phase shifts at the IRIS. In doing so, we derive a closed-form ergodic achievable sum rate expression by leveraging the channel statistical properties. Aiming to maximize this rate, we further propose a deep reinforcement learning (DRL) method via deep deterministic policy gradient (DDPG) for a joint parameter design for antenna activation, beamforming, and power allocation, while properly incorporating the physical constraints on the total transmit power budget. Simulation results verify that our DRL-based algorithm learns an optimal policy from its environment, and outperforms the state-of-the-art benchmarks in real situations, where the statistical CSI varies less and is more stable to obtain than the instantaneous CSI.