Unequally Sub-connected Architecture for Hybrid Beamforming in Massive MIMO Systems

TL;DR

This paper introduces an innovative unequal sub-connected hybrid beamforming architecture for massive MIMO systems, improving achievable rates with reduced complexity and marginal power increase, compared to traditional equal sub-connected designs.

Contribution

It proposes a novel unequal antenna allocation scheme for hybrid beamforming, including analytical design, low-complexity algorithms, and demonstrates performance gains through simulations.

Findings

Up to 10% increase in achievable rate with unequal antenna allocation.

Proposed algorithms significantly reduce computational complexity.

Marginal power increase needed for performance enhancement.

Abstract

A variety of hybrid analog-digital beamforming architectures have recently been proposed for massive multiple-input multiple-output (MIMO) systems to reduce energy consumption and the cost of implementation. In the analog processing network of these architectures, the practical sub-connected structure requires lower power consumption and hardware complexity than the fully connected structure but cannot fully exploit the beamforming gains, which leads to a loss in overall performance. In this work, we propose a novel unequal sub-connected architecture for hybrid combining at the receiver of a massive MIMO system that employs unequal numbers of antennas in sub-antenna arrays. The optimal design of the proposed architecture is analytically derived, and includes antenna allocation and channel ordering schemes. Simulation results show that an enhancement of up to 10% can be attained in the total achievable rate by unequally assigning antennas to sub-arrays in the sub-connected system at the cost of a marginal increase in power consumption. Furthermore, in order to reduce the computational complexity involved in finding the optimal number of antennas connected to each radio frequency (RF) chain, we propose three low-complexity antenna allocation algorithms. The simulation results show that they can yield a significant reduction in complexity while achieving near-optimal performance.