UAV-BS Integration with Urban Infrastructure: An Energy Efficiency Perspective

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dc.contributor.authorKirubakaran, Balajics
dc.contributor.authorVikhrova, Olgacs
dc.contributor.authorAndreev, Sergeycs
dc.contributor.authorHošek, Jiřícs
dc.coverage.issue3cs
dc.coverage.volume63cs
dc.date.accessioned2025-04-04T11:56:31Z
dc.date.available2025-04-04T11:56:31Z
dc.date.issued2024-11-04cs
dc.description.abstractThe integration of uncrewed aerial vehicles (UAVs) with fifth-generation (5G) cellular networks has been a prominent research focus in recent years and continues to attract significant interest in the context of sixth-generation (6G) wireless networks. UAVs can serve as aerial wireless platforms to provide on-demand coverage, mobile edge computing, and enhanced sensing and communication services. However, UAV-assisted networks present new opportunities and challenges due to the inherent size, weight, and power constraints of UAVs, their controllable mobility, and the line-ofsight (LoS) characteristics of communication channels. This article discusses these opportunities and challenges from the viewpoint of mobile network operators (MNOs), and offers a novel perspective on efficiently utilizing modern city infrastructures for UAV deployment in typical urban scenarios. In these scenarios, UAV-mounted base stations (UAV-BSs) can significantly improve service continuity and network energy efficiency. We compare system performance in terms of user satisfaction and energy efficiency between conventional UAV deployment, which follows demand dynamics, and an alternative approach where UAVs land on urban infrastructure equipped with charging stations. To identify the preferred UAV locations, while considering the limited availability of such stations and environmental dynamics, we employ a data-driven genetic algorithm. This algorithm closely approximates the true optimal locations subject to a moderate computational budget.en
dc.formattextcs
dc.format.extent100-106cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE COMMUNICATIONS MAGAZINE. 2024, vol. 63, issue 3, p. 100-106.en
dc.identifier.doi10.1109/MCOM.001.2400247cs
dc.identifier.issn0163-6804cs
dc.identifier.orcid0000-0002-2234-140Xcs
dc.identifier.orcid0000-0002-8382-9185cs
dc.identifier.other191344cs
dc.identifier.researcheridB-1780-2010cs
dc.identifier.scopus37031030200cs
dc.identifier.urihttps://hdl.handle.net/11012/250747
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE COMMUNICATIONS MAGAZINEcs
dc.relation.urihttps://ieeexplore.ieee.org/abstract/document/10742573cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0163-6804/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectGenetic algorithmsen
dc.subjectAutonomous aerial vehiclesen
dc.subjectOptimizationen
dc.subjectHeuristic algorithmsen
dc.subjectWireless communicationen
dc.subjectEnergy efficiencyen
dc.subjectBatteriesen
dc.subjectWireless sensor networksen
dc.subjectVehicle dynamicsen
dc.subjectSensorsen
dc.titleUAV-BS Integration with Urban Infrastructure: An Energy Efficiency Perspectiveen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-191344en
sync.item.dbtypeVAVen
sync.item.insts2025.04.04 13:56:31en
sync.item.modts2025.04.04 11:32:04en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Fakulta elektrotechniky a komunikačních technologiícs
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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Ústav telekomunikací
Fakulta elektrotechniky a komunikačních technologií