An Efficient Slow-Time Adaptation for Massive MIMO Hybrid Beamforming in mm-Wave Time-Varying Channels

TL;DR

This paper introduces a slow-time adaptive hybrid beamforming method for millimeter-wave massive MIMO systems that improves robustness to channel estimation errors while reducing computational complexity.

Contribution

It proposes a recursive filtering approach for adaptive GEB construction using quantized power levels, enhancing robustness and efficiency in time-varying channels.

Findings

Significant reduction in computational complexity.

Performance close to ideal GEB despite large angular errors.

Improved robustness to channel estimation errors.

Abstract

In this paper, adaptive hybrid beamforming methods are proposed for millimeter-wave range massive multiple-input-multiple-output (MIMO) systems considering single carrier wideband transmission in uplink data mode. A statistical analog beamformer is adaptively constructed in slow-time, while the channel is time-varying and erroneously estimated. A recursive filtering approach is proposed, which aims robustness against estimation errors for generalized eigen-beamformer (GEB). Approximated expressions are obtained for channel covariance matrices that decouple angular spread and center angle of multipath components. With these expressions, modified adaptive construction methods for GEB are proposed, which use only the quantized estimated power levels on angular patches. The performances of the proposed slow-time adaptation techniques for statistical Massive MIMO beamforming are evaluated in terms of the output signal-to-interference-and-noise-ratio (SINR), instantaneous channel estimation and beam accuracy. They are shown to be very efficient such that the computational complexity is significantly reduced while the performance remains almost the same as that of the ideal GEB even in large angular estimation errors.