Beamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning

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 with and without IRS, and the case with 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 Dense V2I network scenario compared to Sparse case.

Keywords

• Intelligent reflecting surface (IRS)
• deep reinforcement learning (DRL)
• vehicle-to-infrastructure communications (V2I)
• mmWave