Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8154
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dc.contributor.authorAksan, Yasin-
dc.contributor.authorYüksel, Melda-
dc.contributor.authorÖzbayoğlu, A. Murat-
dc.date.accessioned2022-01-15T12:58:47Z-
dc.date.available2022-01-15T12:58:47Z-
dc.date.issued2021-
dc.identifier.isbn978-1-7281-9505-6-
dc.identifier.issn1525-3511-
dc.identifier.urihttps://doi.org/10.1109/WCNC49053.2021.9417352-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8154-
dc.descriptionIEEE Wireless Communications and Networking Conference (WCNC) -- MAR 29-APR 01, 2021 -- Nanjing, PEOPLES R CHINAen_US
dc.description.abstractWireless communication with unmanned aerial vehicles (UAVs) will become an integral part of future wireless communication systems. In UAVs real-time data transfer is very critical because UAVs need to be controlled from the ground and their data is also transferred in real time. However, aircraft antennas are prone to airframe shadowing. Aircraft surfaces, on which antennas are placed, obscure the main line-of-sight path. In addition, losses used in link budget analyses show great variability in real time. Therefore, observed end-to-end channels, including all impairments, are in general quite different from theoretical calculations in practice. In this work an end-to-end channel link budget, including all effects, is modelled by applying machine learning methods on measured data obtained during past flights. It is observed that ensemble bagged trees (EBT) and exponential Gaussian process regression (GPR) provide the two best results. Pre-processing data and utilizing raw data are also compared. EBT and exponential GPR can predict the amount of end-to-end losses with 7.49% and 8.07% sensitivity respectively using processed data. When raw data is used as input to the EBT method, it can predict the amount of end-to-end loss with a sensitivity of 2.79%, while a theoretical prediction error is 21.9%.en_US
dc.description.sponsorshipIEEEen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2021 Ieee Wireless Communications and Networking Conference (Wcnc)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAirframe shadowingen_US
dc.subjectchannel characterizationen_US
dc.subjectensemble bagged treesen_US
dc.subjectexponential Gaussian process regressionen_US
dc.subjectlink budget analysisen_US
dc.subjectGaussian-Processesen_US
dc.subjectCommunicationen_US
dc.titleChannel Characterization for Aircraft Integrated Antennas Via Machine Learningen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.wosWOS:000704226500125en_US
dc.identifier.scopus2-s2.0-85119371720en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1109/WCNC49053.2021.9417352-
dc.authorscopusid57190955289-
dc.authorscopusid7006176085-
dc.authorscopusid57344759300-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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