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.
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