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

W. Zhu, H. D. Tuan, E. Dutkiewicz, Y. Fang, H. V. Poor, L. Hanzo
Computer Science, Information Theory, Information Theory (cs.IT), Signal Processing (eess.SP)
2023-12-09 00:00:00
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
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