Data-Driven Mode and Group Selection for Downlink MU-MIMO With Implementation in Commodity 802.11ac Network

Multi-user MIMO (MU-MIMO) is a technique that improves spectral efficiency by allowing concurrent communication between one access point (AP) and multiple clients. In practice, the expected gain is not always achieved and is sometimes even negative. We experimentally demonstrate that the downlink MU-MIMO performance in a practical network not only depends on the client's channel but is also influenced by factors that are not captured by conventional models, such as client motion and device type. We propose a data-driven algorithm with a low computational complexity that determines whether a client should operate in MU mode and the MU-MIMO group for clients in MU mode. Such a mode and group selection algorithm is based on a sequence of channel state information (CSI), SNR, and client device type. The algorithm can automatically adapt to the motion and characteristics of individual clients. Experimental results using implementation on a commodity 802.11ac AP show that the proposed data-driven mode and group selection algorithm can improve network throughput by up to 35% over existing algorithms based on conventional models. We also show that the proposed data-driven algorithm has limited sensitivity to environmental changes and can be deployed into new environments without retraining.