  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.
