Client Pre-Screening for MU-MIMO in Commodity 802.11ac Networks via Online Learning

Multi-user MIMO (MU-MIMO) is a technique in 802.11ac and 802.11ax that improves spectral efficiency by allowing concurrent communication between one AP and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. Using a commodity 802.11ac AP, we experimentally determine that the inclusion of clients either in motion or with low SNR can cause throughput below that of single-user transmissions. We then propose a pre-screening algorithm using reinforcement learning to predict if a client can benefit from participating in MU-MIMO. Our algorithm is based on a sequence of channel state information (CSI), SNR, and client device type, and can automatically adapt to the motion of individual clients. Experimental results using a commodity AP show that the additional implementation of the pre-screening algorithm alone, without otherwise modifying MU-MIMO client grouping or link parameter selection algorithms, can improve system throughput by up to 40% when half of the clients are moving. Over 20% throughput improvement is maintained when between 25% to 75% of the clients are moving.