SINR Analysis of Opportunistic MIMO-SDMA Downlink Systems with Linear Combining

TL;DR

This paper develops a systematic approach to analyze the asymptotic throughput and scaling laws of MIMO-SDMA downlink systems using SINR, revealing a convergence to Gumbel distribution and a conventional logK scaling law, validated by simulations.

Contribution

It introduces a novel SINR-based analysis framework for MIMO-SDMA systems, deriving standard logK scaling laws and establishing distribution convergence, improving upon previous SIR-based methods.

Findings

SINR distribution converges to Gumbel type.

Scaling law follows logK form.

SINR-based analysis offers higher fidelity.

Abstract

Opportunistic scheduling (OS) schemes have been proposed previously by the authors for multiuser MIMO-SDMA downlink systems with linear combining. In particular, it has been demonstrated that significant performance improvement can be achieved by incorporating low-complexity linear combining techniques into the design of OS schemes for MIMO-SDMA. However, this previous analysis was performed based on the effective signal-to-interference ratio (SIR), assuming an interference-limited scenario, which is typically a valid assumption in SDMA-based systems. It was shown that the limiting distribution of the effective SIR is of the Frechet type. Surprisingly, the corresponding scaling laws were found to follow $\epsilon\log K$ with $0<\epsilon<1$, rather than the conventional $\log\log K$ form. Inspired by this difference between the scaling law forms, in this paper a systematic approach is developed to derive asymptotic throughput and scaling laws based on signal-to-interference-noise ratio (SINR) by utilizing extreme value theory. The convergence of the limiting distribution of the effective SINR to the Gumbel type is established. The resulting scaling law is found to be governed by the conventional $\log\log K$ form. These novel results are validated by simulation results. The comparison of SIR and SINR-based analysis suggests that the SIR-based analysis is more computationally efficient for SDMA-based systems and it captures the asymptotic system performance with higher fidelity.