Optimizing Channel-Statistics-Based Analog Beamforming for Millimeter-Wave Multi-User Massive MIMO Downlink

In this paper, we consider the design of analog beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems, where both link ends are equipped with hybrid digital/analog beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared with the state-of-the-art algorithms.