Wi-Fi meets ML: a survey on improving IEEE 802.11 performance with machine learning

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• dc.contributor.author Szott, Szymon
• dc.contributor.author Kosek-Szott, Katarzyna
• dc.contributor.author Gawłowicz, Piotr
• dc.contributor.author Torres Gómez, Jorge
• dc.contributor.author Bellalta, Boris
• dc.contributor.author Zubow, Anatolij
• dc.contributor.author Dressler, Falko
• dc.date.accessioned 2023-03-03T07:49:01Z
• dc.date.available 2023-03-03T07:49:01Z
• dc.date.issued 2022
• dc.description.abstract Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.
• dc.description.sponsorship This work was supported in part by the National Science Centre, Poland, under Grant DEC-2020/39/I/ST7/01457; in part by the Federal Ministry of Education and Research (BMBF, Germany) Project OTB-5G+ under Grant 16KIS0985; in part by the 6G Research and Innovation Cluster 6G-RIC under Grant 16KISK020K; in part by the Project ML4WIFI funded by the German Research Foundation (DFG) under Grant DR 639/28-1; in part by the Ministerio de Ciencia e Innovación/Agencia Española de Investigación/Fondo Europeo de Desarrollo Regional (MCIU/AEI/FEDER), European Union (UE) under Grant WINDMAL PGC2018-099959-B-I00; and in part by Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) under Grant SGR017-1188.
• dc.format.mimetype application/pdf
• dc.identifier.citation Szott S, Kosek-Szott K, Gawłowicz P, Torres Gómez J, Bellalta B, Zubow A, Dressler F. Wi-Fi meets ML: a survey on improving IEEE 802.11 performance with machine learning. IEEE Commun Surv Tutor. 2022;24(3):1843-93. DOI: 10.1109/COMST.2022.3179242
• dc.identifier.doi http://dx.doi.org/10.1109/COMST.2022.3179242
• dc.identifier.issn 1553-877X
• dc.identifier.uri http://hdl.handle.net/10230/56024
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Communications Surveys & Tutorials. 2022;24(3):1843-93.
• 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 Wi-Fi
• dc.subject.keyword WLAN
• dc.subject.keyword IEEE 802.11
• dc.subject.keyword machine learning
• dc.subject.keyword deep learning
• dc.subject.keyword artificial intelligence
• dc.title Wi-Fi meets ML: a survey on improving IEEE 802.11 performance with machine learning
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion