Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems

TL;DR

This paper introduces an unsupervised learning approach for hybrid beamforming in mmWave MU-MIMO systems using low-resolution phase shifters, reducing hardware costs while maintaining high performance.

Contribution

It proposes a novel phase classification neural network (PCNet) that efficiently designs analog precoders and combiners with low-resolution PSs, outperforming existing methods.

Findings

Superior sum-rate performance compared to state-of-the-art methods

Reduced complexity in hybrid beamforming design

Effective for various low-resolution phase shifter configurations

Abstract

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.