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...

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Veröffentlicht in:Computer communications Jg. 208; S. 158 - 170
Hauptverfasser: Bardou, Anthony, Begin, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2023
Elsevier
Schlagworte:
Artificial Intelligence
Bayesian optimization
Computer Science
IEEE 802.11
Machine Learning
Networking and Internet Architecture
Power control
Spatial reuse
Spatial reuse
IEEE 802.11
Bayesian optimization
Power control
Machine learning
Bayesian Optimization
Power Control
Machine Learning
Spatial Reuse
ISSN:0140-3664, 1873-703X
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Zusammenfassung: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-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. 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.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2023.06.004