Dynamic Channel Acquisition in MU-MIMO

TL;DR

This paper proposes dynamic user scheduling schemes for MU-MIMO systems that optimize throughput while reducing CSI acquisition overhead and delay unfairness, including practical heuristic algorithms that perform near-optimally.

Contribution

It introduces the GAP-based scheduling framework and develops practical, low-complexity heuristic schemes that achieve near-optimal throughput in MU-MIMO systems.

Findings

GAP-rule is throughput-optimal but infeasible in practice.

Proposed heuristic schemes significantly reduce complexity.

Heuristic schemes perform close to the optimal GAP-based schemes.

Abstract

Multiuser multiple-input-multiple-output (MU-MIMO) systems are known to be hindered by dimensionality loss due to channel state information (CSI) acquisition overhead. In this paper, we investigate user-scheduling in MU-MIMO systems on account of CSI acquisition overhead, where a base station dynamically acquires user channels to avoid choking the system with CSI overhead. The genie-aided optimization problem (GAP) is first formulated to maximize the Lyapunov-drift every scheduling step, incorporating user queue information and taking channel fluctuations into consideration. The scheduling scheme based on GAP, namely the GAP-rule, is proved to be throughput-optimal but practically infeasible, and thus serves as a performance bound. In view of the implementation overhead and delay unfairness of the GAP-rule, the T-frame dynamic channel acquisition scheme and the power-law DCA scheme are further proposed to mitigate the implementation overhead and delay unfairness, respectively. Both schemes are based on the GAP-rule and proved throughput-optimal. To make the schemes practically feasible, we then propose the heuristic schemes, queue-based quantized-block-length user scheduling scheme (QQS), T-frame QQS, and power-law QQS, which are the practical versions of the aforementioned GAP-based schemes, respectively. The QQS-based schemes substantially decrease the complexity, and also perform fairly close to the optimum. Numerical results evaluate the proposed schemes under various system parameters.