REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

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dc.contributor.authorGalkin, Borisen
dc.contributor.authorFonseca, Erikaen
dc.contributor.authorAmer, Ramyen
dc.contributor.authorDaSilva, Luiz A.en
dc.contributor.authorDusparic, Ivanaen
dc.date.accessioned2022-01-12T16:49:35Zen
dc.date.available2022-01-12T16:49:35Zen
dc.date.issued2021en
dc.date.updated2022-01-12T16:49:29Zen
dc.description.abstractUnmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. As a consequence, the UAVs may experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.en
dc.description.versionAccepted versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TVT.2021.3126536en
dc.identifier.eissn1939-9359en
dc.identifier.issn0018-9545en
dc.identifier.orcidPereira da Silva, Luiz [0000-0001-6310-6150]en
dc.identifier.urihttp://hdl.handle.net/10919/107569en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject08 Information and Computing Sciencesen
dc.subject09 Engineeringen
dc.subject10 Technologyen
dc.subjectAutomobile Design & Engineeringen
dc.titleREQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Associationen
dc.title.serialIEEE Transactions on Vehicular Technologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2021-11-15en
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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