Hybrid Analog-Digital Beamforming for Massive MIMO Systems

TL;DR

This paper proposes novel hybrid beamforming algorithms for massive MIMO systems that optimize data transmission with fewer RF chains, improving performance and reducing complexity compared to existing methods.

Contribution

It introduces the Alt-MaG framework and MaGiQ method for hybrid precoder design, achieving lower MSE and better approximation of fully-digital precoders in massive MIMO systems.

Findings

Alt-MaG outperforms state-of-the-art methods in MSE reduction.

MaGiQ achieves low complexity and near-optimal solutions in low RF chain scenarios.

Proposed algorithms are compatible with multiple hardware architectures.

Abstract

In massive MIMO systems, hybrid beamforming is an essential technique for exploiting the potential array gain without using a dedicated RF chain for each antenna. In this work, we consider the data phase in a massive MIMO communication process, where the transmitter and receiver use fewer RF chains than antennas. We examine several different fully- and partially connected schemes and consider the design of hybrid beamformers that minimize the estimation error in the data. For the hybrid precoder, we introduce a framework for approximating the optimal fully-digital precoder with a feasible hybrid one. We exploit the fact that the fully-digital precoder is unique only up to a unitary matrix and optimize over this matrix and the hybrid precoder alternately. Our alternating minimization of approximation gap (Alt-MaG) framework improves the performance over state-of-the-art methods with no substantial increase in complexity. In addition, we present a special case of Alt-MaG, minimal gap iterative quantization (MaGiQ), that results in low complexity and lower mean squared error (MSE) than other common methods, in the case of very few RF chains. MaGiQ is shown to coincide with the optimal fully-digital solution in some scenarios. For combiner design, we exploit the structure of the MSE objective and develop a greedy ratio trace maximization technique, that achieves low MSE under various settings. All of our algorithms can be used with multiple hardware architectures.