Multi-armed bandits for spectrum allocation in multi-agent channel bonding WLANs

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• dc.contributor.author Bellalta, Boris
• dc.date.accessioned 2022-06-27T06:31:50Z
• dc.date.available 2022-06-27T06:31:50Z
• dc.date.issued 2021
• dc.description.abstract While dynamic channel bonding (DCB) is proven to boost the capacity of wireless local area networks (WLANs) by adapting the bandwidth on a per-frame basis, its performance is tied to the primary and secondary channel selection. Unfortunately, in uncoordinated high-density deployments where multiple basic service sets (BSSs) may potentially overlap, hand-crafted spectrum management techniques perform poorly given the complex hidden/exposed nodes interactions. To cope with such challenging Wi-Fi environments, in this paper, we first identify machine learning (ML) approaches applicable to the problem at hand and justify why model-free RL suits it the most. We then design a complete RL framework and call into question whether the use of complex RL algorithms helps the quest for rapid learning in realistic scenarios. Through extensive simulations, we derive that stateless RL in the form of lightweight multi-armed-bandits (MABs) is an efficient solution for rapid adaptation avoiding the definition of broad and/or meaningless states. In contrast to most current trends, we envision lightweight MABs as an appropriate alternative to the cumbersome and slowly convergent methods such as Q-learning, and especially, deep reinforcement learning.
• dc.description.sponsorship The work of Sergio Barrachina-Muñoz and Boris Bellalta was supported in part by Cisco, Machine Learning for Wireless Networking in Highly Dynamic Scenarios (WINDMAL), under Grant PGC2018-099959-B-I00 [Ministerio de Ciencia e Innovación (MCIU)/Agencia Estatal de Innovación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Union Europea (UE)] and Grant SGR-2017-1188. The work of Alessandro Chiumento was supported in part by the ECSEL Joint Undertaking (JU) through the Intelligent Secure Trustable Things (InSecTT) Project (https://www.insectt.eu/) under Grant 876038.
• dc.description.sponsorship The work of Sergio Barrachina-Muñoz and Boris Bellalta was supported in part by Cisco, Machine Learning for Wireless Networking in Highly Dynamic Scenarios (WINDMAL), under Grant PGC2018-099959-B-I00 [Ministerio de Ciencia e Innovación (MCIU)/Agencia Estatal de Innovación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Union Europea (UE)] and Grant SGR-2017-1188. The work of Alessandro Chiumento was supported in part by the ECSEL Joint Undertaking (JU) through the Intelligent Secure Trustable Things (InSecTT) Project (https://www.insectt.eu/) under Grant 876038.
• dc.format.mimetype application/pdf
• dc.identifier.citation Barrachina-Muñoz S, Chiumento A, Bellalta B. Multi-armed bandits for spectrum allocation in multi-agent channel bonding WLANs. IEEE Access. 2021;9:133472-90. DOI: 10.1109/ACCESS.2021.3114430
• dc.identifier.doi http://doi.org/10.1109/ACCESS.2021.3114430
• dc.identifier.issn 2169-3536
• dc.identifier.uri http://hdl.handle.net/10230/53597
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Access. 2021;9:133472-90.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-099959-B-I00
• dc.rights This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Channel bonding
• dc.subject.keyword spectrum allocation
• dc.subject.keyword multi-agent
• dc.subject.keyword reinforcement learning
• dc.subject.keyword multi-armed bandit
• dc.title Multi-armed bandits for spectrum allocation in multi-agent channel bonding WLANs
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion