  Hybrid beamforming is key to achieving energy-efficient 5G wireless networks
equipped with massive amount of antennas. Low-resolution data converters bring
yet another degree of freedom to energy efficiency for the state-of-the-art 5G
transceivers. In this work, we consider the design of hybrid precoders for
massive multiple-input multiple-output (MIMO) channels in millimeter-wave
(mmWave) spectrum along with one-bit digital-to-analog converters (DACs) and
finite-quantized phase shifters. In particular, we propose an
alternating-optimization-based precoder design which recursively computes the
covariance of the quantization distortion, and updates the precoders
accordingly. Numerical results verify that the achievable rate improves quickly
through iterations that involve updates to the weight matrix, distortion
covariance of the quantization, and the respective precoders.
