Practical MU-MIMO User Selection On 802.11ac Commodity Networks

Abstract

Multi-User MIMO, the hallmark of IEEE 802.11ac and the upcoming 802.11ax, promises significant throughput gains by supporting multiple concurrent data streams to a group of users. However, identifying the best-throughput MU-MIMO groups in commodity 802.11ac networks poses three major challenges: <i>a</i>) Commodity 802.11ac users do not provide <i>full CSI</i> feedback, which has been widely used for MU-MIMO grouping. <i>b</i>) Heterogeneous channel bandwidth users limit grouping opportunities. <i>c</i>) Limited-resource on APs cannot support computationally and memory expensive operations, required by existing algorithms. Hence, state-of-the-art designs are either not portable in 802.11ac APs, or perform poorly, as shown by our testbed experiments. In this paper, we design and implement <i>MUSE</i>, a lightweight user grouping algorithm, which addresses the above challenges. Our experiments with commodity 802.11ac testbeds show <i>MUSE</i> can achieve high throughput gains over existing designs.