Analysis of a decentralized Bayesian optimization algorithm for improving spatial reuse in dense WLANs

Despite representing the prominent means of accessing the Internet, WLANs remain subject to performance issues, which may be mitigated through more efficient spatial reuse of radio channels. In this perspective, the IEEE 802.11ax amendment enables 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 online learning solution performing local Bayesian optimizations based on Gaussian processes to improve spatial reuse in WLANs. INSPIRE makes no explicit assumptions on the WLANs' topology and favors altruistic behaviors of the access points in their search for adequate configurations of their TX_POWER and OBSS_PD parameters. INSPIRE can easily be extended to work with a limited number of observations to throttle its computational complexity. We demonstrate the superiority of INSPIRE over other state-of-theart 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. Finally, we discuss the configurations recommended by INSPIRE. We show that they comply with an 802.11ax empirical recommendation, and we correlate their values with some graph-based metrics of the WLAN topologies.