AI-driven F-RANs: Integrating Decision Making Considering Different Time Granularities

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dc.contributor.authorDeAlmeida, Jonathan M.en
dc.contributor.authorDaSilva, Luiz A.en
dc.contributor.authorBoth, Cristiano Bonatoen
dc.contributor.authorRalha, Celia G.en
dc.contributor.authorMarotta, Marcelo A.en
dc.date.accessioned2022-01-04T20:12:10Zen
dc.date.available2022-01-04T20:12:10Zen
dc.date.issued2021-06-07en
dc.date.updated2022-01-04T20:12:07Zen
dc.description.abstractCloud and fog-based networks are promising paradigms for vehicular and mobile networks. Fog Radio Access Networks (F-RANs), in particular, can offload computation tasks to the network edge and reduce the latency. Artificial Intelligence (AI) techniques can be used in F-RANs to achieve, for example, enhanced energy efficiency and increased throughput. Nonetheless, the appropriate technique selection must consider the different time granularities at which decision-making occurs in F-RANs. We discuss the benefits and challenges of implementing an AI-driven F-RAN considering different timescales, highlighting key Machine Learning (ML) techniques for each granularity. Finally, we discuss the challenges and opportunities to integrate different ML solutions in F-RANs.en
dc.description.versionAccepted versionen
dc.format.extentPages 137-148en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/MVT.2021.3078417en
dc.identifier.eissn1556-6080en
dc.identifier.issn1556-6072en
dc.identifier.issue3en
dc.identifier.urihttp://hdl.handle.net/10919/107371en
dc.identifier.volume16en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectNetworking & Telecommunicationsen
dc.titleAI-driven F-RANs: Integrating Decision Making Considering Different Time Granularitiesen
dc.title.serialIEEE Vehicular Technology Magazineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen

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