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

Author:

W. Zhu, H. D. Tuan, E. Dutkiewicz, Y. Fang, H. V. Poor, L. Hanzo

Keyword:

Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)

journal:

date:

2023-12-09 00:00:00

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.

PDF: Long-Term Rate-Fairness-Aware Beamforming Based Massive MIMO Systems.pdf