Beamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning
Yeongrok Lee, Ju-Hyung Lee, Young‐Chai Ko
Abstract
Intelligent reflecting surface (IRS), which can provide a propagation path where non-line-of-sight (NLOS) link exists, is a promising technology to enable beyond fifth-generation (B5G) mobile communication systems. In this paper, we jointly optimize the base station (BS) and IRS beamforming to enhance network performance in the mmWave vehicle-to-infrastructure (V2I) communication system. However, the joint optimization of the beamforming matrix for BS and IRS is challenging due to non-convex and time-varying issues. To tackle those issues, we propose a novel reinforcement learning algorithm based on deep deterministic policy gradient (DDPG) method. Simulation results corroborate that the proposed algorithm converges in both systems <i>with</i> and <i>without</i> IRS, and the case <i>with</i> IRS improves the network performance from as little as about 5% to as much as about 100% depending on the environments such as the number of vehicles or deployment. Simulation results also show that in the IRS-assisted communication, up to 10% higher network throughput can be achieved in <i>Dense</i> V2I network scenario compared to <i>Sparse</i> case.