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dc.contributor.author | Wilhelmi Roca, Francesc |
dc.contributor.author | Barrachina Muñoz, Sergio |
dc.contributor.author | Bellalta, Boris |
dc.contributor.author | Cano Bastidas, Cristina |
dc.contributor.author | Jonsson, Anders, 1973- |
dc.contributor.author | Ram, Vishnu |
dc.date.accessioned | 2020-04-27T08:27:26Z |
dc.date.issued | 2020 |
dc.identifier.citation | Wilhelmi F, Barrachina Muñoz S, Bellalta B, Cano C, Jonsson A. Ram V. A flexible machine-learning-aware architecture for future WLANs. IEEE Commun Mag. 2020 Mar 18;58(3):25-31. DOI: 10.1109/MCOM.001.1900637 |
dc.identifier.issn | 0163-6804 |
dc.identifier.uri | http://hdl.handle.net/10230/44337 |
dc.description.abstract | Lots of hopes have been placed on machine learning (ML) as a key enabler of future wireless networks. By taking advantage of large volumes of data, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networks are not yet prepared to support the ensuing requirements of ML-based applications in terms of data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. Specifically, we look into wireless local area networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. In particular, we propose to adopt the International Telecommunication Union (ITU) unified architecture for 5G and beyond. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs. Finally, we showcase the superiority of the architecture through an ML-enabled use case for future networks. |
dc.description.sponsorship | This work has been partially supported by grants MDM2015-0502, 2017-SGR-11888, by WINDMAL PGC2018-099959-B-I00 (MCIU/AEI/FEDER,UE), by a Gift from the Cisco University Research Program (CG#890107) Fund, and by SPOTS project (RTI2018-095438-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities. The work by Sergio Barrachina-Munoz is supported by an FI grant from Generalitat de Catalunya. |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
dc.relation.ispartof | IEEE Communications Magazine. 2020 Mar 18;58(3):25-31 |
dc.rights | © 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/MCOM.001.1900637 |
dc.title | A flexible machine-learning-aware architecture for future WLANs |
dc.type | info:eu-repo/semantics/article |
dc.identifier.doi | http://dx.doi.org/10.1109/MCOM.001.1900637 |
dc.subject.keyword | Computer architecture |
dc.subject.keyword | 5G mobile communication |
dc.subject.keyword | Wireless networks |
dc.subject.keyword | ITU |
dc.subject.keyword | Machine learning |
dc.subject.keyword | IEEE 802.11 Standard |
dc.subject.keyword | Wireless LAN |
dc.rights.accessRights | info:eu-repo/semantics/openAccess |
dc.type.version | info:eu-repo/semantics/acceptedVersion |