Carrascosa Zamacois, Marc2020-11-162020-11-162020Carrascosa M, Bellalta B. Decentralized AP selection using multi-armed bandits: opportunistic ε-greedy with stickiness. In: Proceedings of the 2019 IEEE Symposium on Computers and Communications (ISCC); 2019 Jun 29-Jul 3; Barcelona, Spain. [New York]: IEEE; 2020. [7 p.] DOI: 10.1109/ISCC47284.2019.8969724978-1-7281-2999-02642-7389http://hdl.handle.net/10230/45780Comunicació presentada a: IEEE Symposium on Computers and Communications (ISCC) celebrat del 29 de juny al de 3 juliol de 2019 a Barcelona, Espanya.WiFi densification leads to the existence of multiple overlapping coverage areas, which allows user stations (STAs) to choose between different Access Points (APs). The standard WiFi association method makes the STAs select the AP with the strongest signal, which in many cases leads to underutilization of some APs while overcrowding others. To mitigate this situation, Reinforcement Learning techniques such as Multi-Armed Bandits can be used to dynamically learn the optimal mapping between APs and STAs, and so redistribute the STAs among the available APs accordingly. This is an especially challenging problem since the network response observed by a given STA depends on the behavior of the others, and so it is very difficult to predict without a global view of the network. In this paper, we focus on solving this problem in a decentralized way, where STAs independently explore the different APs inside their coverage range, and select the one that better satisfy its needs. To do it, we propose a novel approach called opportunistic ε-greedy with Stickiness that halts the exploration when a suitable AP is found, then, the STA remains associated to that same AP while it is satisfied, only resuming the exploration after several unsatisfactory association periods. With this approach, we reduce significantly the network response variability, improving the ability of the STAs to find a solution faster, as well as achieving a more efficient use of the network resources.application/pdfeng© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/ISCC47284.2019.8969724info:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/ISCC47284.2019.8969724IEEE 802.11WLANsReinforcement LearningMulti-Armed Banditsinfo:eu-repo/semantics/openAccess