Machine learning and Wi-Fi: unveiling the path toward AI/ML-Native IEEE 802.11 networks

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• dc.contributor.author Wilhelmi Roca, Francesc
• dc.contributor.author Szott, Szymon
• dc.contributor.author Kosek-Szott, Katarzyna
• dc.contributor.author Bellalta, Boris
• dc.date.accessioned 2025-10-29T09:25:39Z
• dc.date.available 2025-10-29T09:25:39Z
• dc.date.issued 2025
• dc.date.updated 2025-10-29T09:25:39Z
• dc.description.abstract Artificial intelligence (AI) and machine learning (ML) are nowadays mature technologies considered essential for driving the evolution of future communications systems. Simultaneously, Wi-Fi technology has constantly evolved over the past three decades and incorporated new features generation after generation, thus gaining in complexity. As such, researchers have observed that AI/ML functionalities may be required to address the upcoming Wi-Fi challenges that will be otherwise difficult to solve with traditional approaches. This article discusses the role of AI/ML in current and future Wi-Fi networks, and depicts the ways forward. A roadmap toward AI/ML-native Wi-Fi, key challenges, standardization efforts, and major enablers are also discussed. An exemplary use case is provided to showcase the potential of AI/ ML in Wi-Fi at different adoption stages.
• dc.description.sponsorship This paper is supported by the CHIST-ERA Wireless AI 2022 call MLDR project (ANR-23-CHR4-0005), partially funded by AEI and NCN under projects PCI2023-145958-2 and 2023/05/Y/ST7/00004, respectively. B. Bellalta's contribution is supported by Wi-XR PID2021-123995NB-I00 (MCIU/AEI/FEDER,UE) and MdM CEX2021-001195-M/AEI/10.13039/501100011033.
• dc.format.mimetype application/pdf
• dc.identifier.citation Wilhelmi F, Szott S, Kosek-Szott K, Bellalta B. Machine learning and Wi-Fi: unveiling the path toward AI/ML-Native IEEE 802.11 networks. IEEE Commun Mag. 2025 Jul;63(7):114-20. DOI: 10.1109/MCOM.001.2400292
• dc.identifier.doi http://dx.doi.org/10.1109/MCOM.001.2400292
• dc.identifier.issn 0163-6804
• dc.identifier.uri http://hdl.handle.net/10230/71688
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Communications Magazine. 2025 Jul;63(7):114-20
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2021-123995NB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PCI2023-145958-2
• 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 http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Wireless fidelity
• dc.subject.keyword Artificial intelligence
• dc.subject.keyword IEEE 802.11 standard
• dc.subject.keyword Computational modeling
• dc.subject.keyword 3GPP
• dc.subject.keyword Costs
• dc.subject.keyword Standards
• dc.subject.keyword Data models
• dc.subject.keyword Protocols
• dc.subject.keyword Computer architecture
• dc.subject.keyword Machine learning
• dc.title Machine learning and Wi-Fi: unveiling the path toward AI/ML-Native IEEE 802.11 networks
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion