Optimizing flying base station connectivity by RAN slicing and reinforcement learning

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorCarrillo Melgarejo, Dick-
Autor(es): dc.creatorPokorny, Jiri-
Autor(es): dc.creatorSeda, Pavel-
Autor(es): dc.creatorNarayanan, Arun-
Autor(es): dc.creatorNardelli, Pedro H. J.-
Autor(es): dc.creatorRasti, Mehdi-
Autor(es): dc.creatorHosek, Jiri-
Autor(es): dc.creatorSeda, Milos-
Autor(es): dc.creatorRodríguez, Demóstenes Z.-
Autor(es): dc.creatorKoucheryavy, Yevgeni-
Autor(es): dc.creatorFraidenraich, Gustavo-
Data de aceite: dc.date.accessioned2026-02-09T12:13:01Z-
Data de disponibilização: dc.date.available2026-02-09T12:13:01Z-
Data de envio: dc.date.issued2022-10-26-
Data de envio: dc.date.issued2022-10-26-
Data de envio: dc.date.issued2022-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/55353-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1156801-
Descrição: dc.descriptionThe application of flying base stations (FBS) in wireless communication is becoming a key enabler to improve cellular wireless connectivity. Following this tendency, this research work aims to enhance the spectral efficiency of FBSs using the radio access network (RAN) slicing framework; this optimization considers that FBSs’ location was already defined previously. This framework splits the physical radio resources into three RAN slices. These RAN slices schedule resources by optimizing individual slice spectral efficiency by using a deep reinforcement learning approach. The simulation indicates that the proposed framework generally outperforms the spectral efficiency of the network that only considers the heuristic predefined FBS location, although the gains are not always significant in some specific cases. Finally, spectral efficiency is analyzed for each RAN slice resource and evaluated in terms of service-level agreement (SLA) to indicate the performance of the framework.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourceIEEE Access-
Palavras-chave: dc.subjectFlying base stations-
Palavras-chave: dc.subjectUnmanned aerial vehicles (UAVs)-
Palavras-chave: dc.subjectLocation optimization-
Palavras-chave: dc.subjectWireless communication-
Palavras-chave: dc.subjectDeep-reinforcement learning-
Palavras-chave: dc.subjectEstações-bases voadoras-
Palavras-chave: dc.subjectVeículos aéreos não tripulados (VANTs)-
Palavras-chave: dc.subjectComunicações sem fio-
Palavras-chave: dc.subjectAprendizagem por reforço profundo-
Título: dc.titleOptimizing flying base station connectivity by RAN slicing and reinforcement learning-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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