1. Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems
- Author
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Kuo, Chia-Ho, Chang, Hsin-Yuan, Chang, Ronald Y., and Chung, Wei-Ho
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Information Theory ,Computer Science - Machine Learning - 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., Comment: IEEE International Conference on Communications (ICC) 2022
- Published
- 2022