Unsupervised Learning Based Hybrid Beamforming with Low-Resolution Phase Shifters for MU-MIMO Systems
This addresses hardware cost and power consumption issues in 5G and beyond mmWave communications, though it is incremental as it builds on existing hybrid beamforming methods.
The paper tackles the impracticality of infinite-resolution phase shifters in hybrid beamforming for MU-MIMO systems by proposing an unsupervised learning scheme that transforms the design into a phase classification problem, achieving superior sum-rate and complexity performance compared to state-of-the-art designs for low-resolution configurations.
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.