  WLANs, which have overtaken wired networks to become the primary means of
connecting devices to the Internet, are prone to performance issues due to the
scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and
subsequent amendments aim at increasing the spatial reuse of a radio channel by
allowing the dynamic update of two key parameters in wireless transmission: the
transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this
paper, we present INSPIRE, a distributed solution performing local Bayesian
optimizations based on Gaussian processes to improve the spatial reuse in
WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and
favors altruistic behaviors of the access points, leading them to find adequate
configurations of their TX_POWER and OBSS_PD parameters for the "greater good"
of the WLANs. We demonstrate the superiority of INSPIRE over other
state-of-the-art strategies using the ns-3 simulator and two examples inspired
by real-life deployments of dense WLANs. Our results show that, in only a few
seconds, INSPIRE is able to drastically increase the quality of service of
operational WLANs by improving their fairness and throughput.

Networking and Internet Architecture;Machine Learning