LAUSR

User selection is one of the most important components for next generation multi-user multiple-input-multiple-output wireless local area networks. However, state-of-the-art approaches neglect the heterogeneity of users in the available bandwidth and the number of antennas, which diminishes their performance considerably. To tackle this challenge, we formulate a novel integer optimization framework to select the antennas of heterogeneous users simultaneously. With estimated signal-to-interference-and-noise ratio of users via channel vector projection, we propose a low-complexity branch-and-prune algorithm to search for the near-optimal combinations of user antennas. Our algorithm is compatible with legacy 802.11ac and is implemented on the software defined radio system. Extensive experiments show that our algorithm achieves around 95% of the optimal throughput and outperforms a benchmark scheme with a $1.18\boldsymbol \times $ gain in realistic indoor environments.