Machine Learning-based Fingerprinting Localization in 5G Cellular Networks

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorDinh Le, Thao
dc.contributor.authorMašek, Pavel
dc.date.accessioned2024-07-09T07:47:51Z
dc.date.available2024-07-09T07:47:51Z
dc.date.issued2024cs
dc.description.abstractThis study explores the viability of employing machine learning (ML)-based fingerprinting localization in 5G heterogeneous cellular networks. We conducted an extensive measurement campaign to collect data and utilized them to train three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). The findings reveal that RF delivers the highest accuracy among the three ML algorithms. Furthermore, the results indicate that 5G New Radio (NR) can benefit the most from this localization method due to the dense deployment of base stations, achieving median localization errors of 17.5 m and 106 m during the validation and testing phases, respectively.en
dc.formattextcs
dc.format.extent222-226cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 222-226. ISBN 978-80-214-6230-4cs
dc.identifier.doi10.13164/eeict.2024.222
dc.identifier.isbn978-80-214-6230-4
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249320
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectFingerprinting Localizationen
dc.subjectMachine Learningen
dc.subject5G New Radioen
dc.subjectNB-IoTen
dc.subjectLTE-Men
dc.subjectRandom Foresten
dc.subjectXGBoosten
dc.subjectSupport Vector Machineen
dc.titleMachine Learning-based Fingerprinting Localization in 5G Cellular Networksen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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