Large Deviation Delay Analysis of Queue-Aware Multi-user MIMO Systems with Multi-timescale Mobile-Driven Feedback

TL;DR

This paper introduces a queue-aware, multi-timescale feedback scheduling algorithm for MU-MIMO systems that significantly improves delay performance by leveraging large deviation analysis, outperforming CSI-only methods with minimal feedback.

Contribution

It proposes a novel two-stage queue-aware scheduling algorithm with large deviation analysis to enhance delay performance in MU-MIMO systems.

Findings

Large deviation decay rate is significantly higher with the proposed algorithm.

The algorithm outperforms CSI-only scheduling with less feedback.

Numerical results confirm improved queueing delay performance.

Abstract

Multi-user multi-input-multi-output (MU-MIMO) systems transmit data to multiple users simultaneously using the spatial degrees of freedom with user feedback channel state information (CSI). Most of the existing literatures on the reduced feedback user scheduling focus on the throughput performance and the user queueing delay is usually ignored. As the delay is very important for real-time applications, a low feedback queue-aware user scheduling algorithm is desired for the MU-MIMO system. This paper proposed a two-stage queue-aware user scheduling algorithm, which consists of a queue-aware mobile-driven feedback filtering stage and a SINR-based user scheduling stage, where the feedback filtering policy is obtained from the solution of an optimization problem. We evaluate the queueing performance of the proposed scheduling algorithm by using the sample path large deviation analysis. We show that the large deviation decay rate for the proposed algorithm is much larger than that of the CSI-only user scheduling algorithm. The numerical results also demonstrate that the proposed algorithm performs much better than the CSI-only algorithm requiring only a small amount of feedback.