Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10338
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dc.contributor.authorDemirtaş, Ali Murat-
dc.contributor.authorSeyfioğlu, Mehmet Saygın-
dc.contributor.authorBor-Yalınız, İrem-
dc.contributor.authorTavlı, Bülent-
dc.contributor.authorYanikomeroğlu, Halim-
dc.date.accessioned2023-04-16T10:00:20Z-
dc.date.available2023-04-16T10:00:20Z-
dc.date.issued2023-
dc.identifier.issn2162-2248-
dc.identifier.issn2162-2256-
dc.identifier.urihttps://doi.org/10.1109/MCE.2022.3201366-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10338-
dc.description.abstractTo address the ever-growing connectivity demand in communications, the adoption of ingenious solutions, such as utilization of unmanned aerial vehicles (UAVs) as mobile base stations, is imperative. In general, the location of a UAV base station (UAV-BS) is determined by optimization algorithms, which have high computationally complexities and are hard to run on UAVs due to energy consumption and time constraints. In this article, we overview the UAV-BS trajectory optimization problem for next generation wireless networks and show that a convolutional neural network (CNN) model can be trained to infer the location of a UAV-BS in real time. To this end, we create a framework to determine the UAV-BS locations considering the deployment of mobile users (MUs) to generate labels by using the data obtained from an optimization algorithm. Performance evaluations reveal that once the CNN model is trained with the given labels and locations of MUs, the proposed approach is, indeed, capable of approximating the results given by the adopted optimization algorithm with high fidelity, outperforming reinforcement learning-based approaches in resource-constrained settings. We also explore future research challenges and highlight key issues.en_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Consumer Electronics Magazineen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectQuality of serviceen_US
dc.subjectConvolutional neural networksen_US
dc.subjectTrajectory optimizationen_US
dc.subjectConsumer electronicsen_US
dc.subjectTrainingen_US
dc.subjectMemory managementen_US
dc.subjectEnergy dissipationen_US
dc.subjectArtificial-Intelligenceen_US
dc.subjectWireless Networksen_US
dc.subjectModelen_US
dc.titleDeep Learning-Based Autonomous UAV-BSs for NGWNs: Overview and a Novel Architectureen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.volume12en_US
dc.identifier.issue1en_US
dc.identifier.startpage32en_US
dc.identifier.endpage42en_US
dc.identifier.wosWOS:000927796400009en_US
dc.identifier.scopus2-s2.0-85137574535en_US
dc.institutionauthor-
dc.identifier.doi10.1109/MCE.2022.3201366-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
local.message.claim2023-09-21T14:50:32.528+0300|||rp00158|||submit_approve|||dc_contributor_author|||None*
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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