Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning

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dc.contributor.authorMelgarejo, Dick Carrillocs
dc.contributor.authorPokorný, Jiřícs
dc.contributor.authorŠeda, Pavelcs
dc.contributor.authorNarayanan, Aruncs
dc.contributor.authorNardelli, Pedro Henrique Julianocs
dc.contributor.authorRasti, Mehdics
dc.contributor.authorHošek, Jiřícs
dc.contributor.authorŠeda, Milošcs
dc.contributor.authorRodríguez, Demóstenes Zegarracs
dc.contributor.authorKoucheryavy, Yevgenics
dc.contributor.authorFraidenraich, Gustavocs
dc.coverage.issue1cs
dc.coverage.volume10cs
dc.date.accessioned2022-07-01T10:52:27Z
dc.date.available2022-07-01T10:52:27Z
dc.date.issued2022-05-17cs
dc.description.abstractThe 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.en
dc.formattextcs
dc.format.extent53746-53760cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Access. 2022, vol. 10, issue 1, p. 53746-53760.en
dc.identifier.doi10.1109/ACCESS.2022.3175487cs
dc.identifier.issn2169-3536cs
dc.identifier.other177853cs
dc.identifier.urihttp://hdl.handle.net/11012/208143
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Accesscs
dc.relation.urihttps://ieeexplore.ieee.org/document/9775679cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2169-3536/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectFlying Base Stationsen
dc.subjectUAVsen
dc.subjectLocation Optimizationen
dc.subjectWireless Communicationen
dc.subjectDeep-reinforcement Learningen
dc.titleOptimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177853en
sync.item.dbtypeVAVen
sync.item.insts2022.09.16 16:50:28en
sync.item.modts2022.09.16 16:16:26en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
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