Intelligent Base Station Association for UAV Cellular Users: A Supervised Learning Approach

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dc.contributor.authorGalkin, Borisen
dc.contributor.authorAmer, Ramyen
dc.contributor.authorFonseca, Erikaen
dc.contributor.authorDaSilva, Luiz A.en
dc.date.accessioned2022-01-04T20:35:50Zen
dc.date.available2022-01-04T20:35:50Zen
dc.date.issued2020-01-01en
dc.date.updated2022-01-04T20:35:49Zen
dc.description.abstractFifth Generation (5G) cellular networks are expected to provide cellular connectivity for vehicular users, including Unmanned Aerial Vehicles (UAVs). When flying in the air, these users experience strong, unobstructed channel conditions to a large number of Base Stations (BSs) on the ground. This creates very strong interference conditions for the UAV users, while at the same time offering them a large number of BSs to potentially associate with for cellular service. Therefore, to maximise the performance of the UAV-BS wireless link, the UAV user needs to be able to choose which BSs to connect to, based on the observed environmental conditions. This paper proposes a supervised learning-based association scheme, using which a UAV can intelligently associate with the most appropriate BS. We train a Neural Network (NN) to identify the most suitable BS from several candidate BSs, based on the received signal powers from the BSs, known distances to the BSs, as well as the known locations of potential interferers. We then compare the performance of the NN-based association scheme against strongest-signal and closest-neighbour association schemes, and demonstrate that the NN scheme significantly outperforms the simple heuristic schemes.en
dc.description.versionAccepted versionen
dc.format.extentPages 383-388en
dc.format.extent6 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/107383en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000654691900067&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectComputer Science, Information Systemsen
dc.subjectComputer Science, Theory & Methodsen
dc.subjectEngineering, Electrical & Electronicen
dc.subjectTelecommunicationsen
dc.subjectComputer Scienceen
dc.subjectEngineeringen
dc.subjectCellular-connected UAVsen
dc.subjectMachine learningen
dc.subjectSupervised Learningen
dc.titleIntelligent Base Station Association for UAV Cellular Users: A Supervised Learning Approachen
dc.title.serial2020 IEEE 3rd 5G World Forum (5GWF)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherMeetingen
dc.type.otherBooken
pubs.finish-date2020-10-10en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.start-date2020-09-10en

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