Low-Complexity Hybrid Beamforming for Multi-Cell mmWave Massive MIMO: A Primitive Kronecker Decomposition Approach

TL;DR

This paper proposes a low-complexity hybrid beamforming method for multi-cell mmWave massive MIMO systems using primitive Kronecker decomposition, achieving near-optimal sum rate with reduced hardware and computational costs.

Contribution

It introduces a novel hybrid beamforming design based on primitive Kronecker decomposition and dynamic factor allocation, improving efficiency and reducing complexity in multi-cell mmWave MIMO systems.

Findings

Achieves sum rate close to optimal digital MMSE scheme.

Reduces hardware cost and computational complexity.

Provides an optimality condition for antenna configuration.

Abstract

To circumvent the high path loss of mmWave propagation and reduce the hardware cost of massive multiple-input multiple-output antenna systems, full-dimensional hybrid beamforming is critical in 5G and beyond wireless communications. Concerning an uplink multi-cell system with a large-scale uniform planar antenna array, this paper designs an efficient hybrid beamformer using primitive Kronecker decomposition and dynamic factor allocation, where the analog beamformer applies to null the inter-cell interference and simultaneously enhances the desired signals. In contrast, the digital beamformer mitigates the intra-cell interference using the minimum mean square error (MMSE) criterion. Then, due to the low accuracy of phase shifters inherent in the analog beamformer, a low-complexity hybrid beamformer is developed to slow its adjustment speed. Next, an optimality analysis from a subspace perspective is performed, and a sufficient condition for optimal antenna configuration is established. Finally, simulation results demonstrate that the achievable sum rate of the proposed beamformer approaches that of the optimal pure digital MMSE scheme, yet with much lower computational complexity and hardware cost.