  We propose and experimentally evaluate a novel method that dynamically
changes the contention window of access points based on system load to improve
performance in a dense Wi-Fi deployment. A key feature is that no MAC protocol
changes, nor client side modifications are needed to deploy the solution. We
show that setting an optimal contention window can lead to throughput and
latency improvements up to 155%, and 50%, respectively. Furthermore, we devise
an online learning method that efficiently finds the optimal contention window
with minimal training data, and yields an average improvement in throughput of
53-55% during congested periods for a real traffic-volume workload replay in a
Wi-Fi test-bed.
