Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems

TL;DR

This paper introduces a novel long-term rate-fairness-aware beamforming approach for massive MIMO systems, optimizing SLNR-based objectives to improve user fairness in ergodic rate distribution.

Contribution

It presents the first MU beamforming method for long-term rate-fairness in massive MIMO, with convex optimization algorithms and closed-form solutions for scalable implementation.

Findings

Improved fairness in ergodic rate distribution among users

Convex-solver based algorithm with cubic complexity per iteration

Closed-form algorithms with scalable complexity

Abstract

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in fulldimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users' signal-toleakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR maxmin problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GMSLNR and of the SLNR soft max-min problem. The simulations provided confirm the users' improved-fairness ergodic rate distributions.