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

Yeongrok Lee, Ju Hyung Lee, Young Chai Ko

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)60521-60533
Number of pages13
JournalIEEE Access
Publication statusPublished - 2022


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

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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