Low-complexity separable beamformers for massive antenna array systems

TL;DR

This paper introduces low-complexity Kronecker-separable beamformers for massive bi-dimensional antenna arrays, balancing computational efficiency with source recovery accuracy depending on source separation.

Contribution

It proposes novel optimization strategies for Kronecker-separable beamformers exploiting array geometry, reducing computational costs in massive array systems.

Findings

Methods are computationally efficient.

Source recovery degrades with closely spaced sources.

Degradation is negligible when sources are well separated.

Abstract

Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low-complexity signal processing methods. We propose optimising a Kronecker-separable beamforming filter that takes advantage of the bi-dimensional array geometry to reduce computational costs. The Kronecker factors are obtained using two strategies: alternating optimisation, and sub-array minimum mean square error beamforming with Tikhonov regularization. According to the simulation results, the proposed methods are computationally efficient but come with source recovery degradation, which becomes negligible when the sources are sufficiently separated in space.