Combining software defined networks and machine learning to enable self organizing WLANs

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• dc.contributor.author López Raventós, Álvaro
• dc.contributor.author Wilhelmi Roca, Francesc
• dc.contributor.author Barrachina Muñoz, Sergio
• dc.contributor.author Bellalta, Boris
• dc.date.accessioned 2021-05-26T07:41:24Z
• dc.date.available 2021-05-26T07:41:24Z
• dc.date.issued 2019
• dc.description Comunicació presentada al International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2019), celebrat del 21 al 23 d'octubre de 2020 a Barcelona, Espanya.
• dc.description.abstract Next generation of wireless local area networks (WLANs) will operate in dense, chaotic and highly dynamic scenarios that in a significant number of cases may result in a low user experience due to uncontrolled high interference levels. Flexible network architectures, such as the software-defined networking (SDN) paradigm, will provide WLANs with new capabilities to deal with users' demands, while achieving greater levels of efficiency and flexibility in those complex scenarios. On top of SDN, the use of machine learning (ML) techniques may improve network resource usage and management by identifying feasible configurations through learning. ML techniques can drive WLANs to reach optimal working points by means of parameter adjustment, in order to cope with different network requirements and policies, as well as with the dynamic conditions. In this paper we overview the work done in SDN for WLANs, as well as the pioneering works considering ML for WLAN optimization. Finally, in order to demonstrate the potential of ML techniques in combination with SDN to improve the network operation, we evaluate different use cases for intelligent-based spatial reuse and dynamic channel bonding operation in WLANs using Multi-Armed Bandits.en
• dc.description.sponsorship This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), by the Catalan Government under grant 2017-SGR-1188, by the Spanish Government under grant PGC2018-099959-B-I00 (MCIU/AEI/FEDER,UE), and by a Gift from the Cisco University Research Program (CG#890107, Towards Deterministic Channel Access in High-Density WLANs) Fund, a corporate advised fund of Silicon Valley Community Foundation. The work done by S. Barrachina-Mu ˜noz is supported by a FI grant from the Generalitat de Catalunya.
• dc.format.mimetype application/pdf
• dc.identifier.citation López-Raventós Á, Wilhelmi F, Barrachina-Muñoz S, Bellalta B. Combining software defined networks and machine learning to enable self organizing WLANs. In: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob); 2019 Oct 21-23; Barcelona, Spain. New Jersey: IEEE; 2019. p. 167-74. DOI: 10.1109/WiMOB.2019.8923569
• dc.identifier.doi http://dx.doi.org/10.1109/WiMOB.2019.8923569
• dc.identifier.issn 2160-4894
• dc.identifier.uri http://hdl.handle.net/10230/47658
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob); 2019 Oct 21-23; Barcelona, Spain. New Jersey: IEEE; 2019. p. 167-74
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-099959-B-I00
• dc.rights © 2019 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/WiMOB.2019.8923569
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Protocolsen
• dc.subject.keyword Wireless networksen
• dc.subject.keyword Interferenceen
• dc.subject.keyword Ethanolen
• dc.subject.keyword Signal to noise ratioen
• dc.subject.keyword Conferencesen
• dc.title Combining software defined networks and machine learning to enable self organizing WLANsen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion