Open AccessBeamforming Optimization for IRS-Assisted mmWave V2I Communication Systems via Reinforcement Learning
Author(s) -
Yeongrok Lee,
Ju-Hyung Lee,
Young-Chai Ko
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3181152
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.