Motion-Aware Optimizations for Downlink MU-MIMO in 802.11ax Networks

Abstract

Multi-User Multiple-Input and Multiple-Output (MU-MIMO) is a technique that allows concurrent transmissions between one access point (AP) and multiple clients to improve spectral efficiency. In practice, however, the MU-MIMO is sensitive to client mobility and is sometimes even harmful to the performance in networks with moving clients. In this paper, we identify that it is essential to optimize the MUMIMO performance with moving clients by jointly selecting the sounding period, the number of spatial streams, and client grouping with the consideration of the client density of the network. We develop a data-driven model that estimates client throughput with the consideration of these parameters, as well as an algorithm that jointly determines the parameters for each client with low computational complexity. Using a commodity 802.11ax network, we experimentally demonstrate the significant impact of the key factors on MU-MIMO performance. Based on experimental data, we develop an emulation model to evaluate network performance with different client densities and mobility. Emulation results show that our proposed algorithm outperforms conventional schemes by over 20% in MU-MIMO networks with moving clients.