Machine Learning-Based Beamforming Design for Millimeter Wave IRS Communications With Discrete Phase Shifters

Wencai Yan, Gangcan Sun, Wanming Hao, Zhengyu Zhu, Zheng Chu, Pei Xiao

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)


In this letter, we investigate an intelligent reflecting surface (IRS)-assisted millimeter-wave multiple-input single-output downlink wireless communication system. By jointly calculating the active beamforming at the base station and the passive beamforming at the IRS, we aim to minimize the transmit power under the constraint of each user' signal-to-interference-plus-noise ratio. To solve this problem, we propose a low-complexity machine learning-based cross-entropy (CE) algorithm to alternately optimize the active beamforming and the passive beamforming. Specifically, in the alternative iteration process, the zero-forcing (ZF) method and CE algorithm are applied to acquire the active beamforming and the passive beamforming, respectively. The CE algorithm starts with random sampling, by the idea of distribution focusing, namely shifting the distribution towards a desired one by minimizing CE, and a near optimal reflection coefficients with adequately high probability can be obtained. In addition, we extend the original one-bit phase shift at the IRS to the common case with high-resolution phase shift to enhance the effectiveness of the algorithms. Simulation results verify that the proposed algorithm can obtain a near optimal solution with lower computational complexity.

Original languageEnglish
Pages (from-to)2467-2471
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number12
Publication statusPublished - 1 Dec 2022
Externally publishedYes


  • Intelligent reflecting surface
  • discrete phase shifts
  • machine learning
  • mmWave

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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