Two-Stage Beamformer Design for Massive MIMO Downlink By Trace Quotient Formulation

TL;DR

This paper introduces a novel two-stage beamformer design for massive MIMO downlink that maximizes a lower bound on average SLNR using trace quotient optimization, based solely on channel statistics.

Contribution

It formulates the outer beamformer design as a trace quotient problem and provides an iterative algorithm for optimal solution, improving performance without relying on instantaneous channel info.

Findings

Significant performance gains over existing methods.

Effective control of signal and interference trade-off.

Trace quotient formulation enables efficient optimization.

Abstract

In this paper, the problem of outer beamformer design based only on channel statistic information is considered for two-stage beamforming for multi-user massive MIMO downlink, and the problem is approached based on signal-to-leakage-plus-noise ratio (SLNR). To eliminate the dependence on the instantaneous channel state information, a lower bound on the average SLNR is derived by assuming zero-forcing (ZF) inner beamforming, and an outer beamformer design method that maximizes the lower bound on the average SLNR is proposed. It is shown that the proposed SLNR-based outer beamformer design problem reduces to a trace quotient problem (TQP), which is often encountered in the field of machine learning. An iterative algorithm is presented to obtain an optimal solution to the proposed TQP. The proposed method has the capability of optimally controlling the weighting factor between the signal power to the desired user and the interference leakage power to undesired users according to different channel statistics. Numerical results show that the proposed outer beamformer design method yields significant performance gain over existing methods.