Analysis of deep learning based path loss prediction from satellite images

dc.authoridAtes, Hasan/0000-0002-6842-1528
dc.authoridGunturk, Bahadir/0000-0003-0779-9620
dc.authorwosidAtes, Hasan/M-5160-2013
dc.authorwosidGunturk, Bahadir/G-1609-2019
dc.contributor.authorBaykaş, Tunçer
dc.contributor.authorAtes, Hasan F.
dc.contributor.authorBaykas, Tuncer
dc.contributor.authorGunturk, Bahadir K.
dc.date.accessioned2023-10-19T15:11:49Z
dc.date.available2023-10-19T15:11:49Z
dc.date.issued2021
dc.department-temp[Alam, Muhammad Z.; Ates, Hasan F.; Gunturk, Bahadir K.] Istanbul Medipol Univ, Sch Engn & Nat Sci, Istanbul, Turkey; [Baykas, Tuncer] Kadir Has Univ, Dept Elect Elect Engn, Istanbul, Turkeyen_US
dc.description29th IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUN 09-11, 2021 -- ELECTR NETWORKen_US
dc.description.abstractDetermining the channel model parameters of a wireless communication system, either by measurements or by running electromagnetic propagation simulations, is a time-consuming process. Any rapid deployment of network demands faster determination of at least major channel parameters. In this paper, we investigate the idea of using deep convolutional neural networks and satellite images for channel parameters (i.e., path loss exponent n and shadowing factor sigma) prediction in a cellular network with aerial base stations. Specifically, we investigate the performance dependency of the method on three different factors: height of the transmitter antenna, quantization levels of the channel parameters and architectural design of CNN. The results presented in this paper show a high prediction accuracy of the channel parameters in real-time.en_US
dc.description.sponsorshipIEEE,IEEE Turkey Secten_US
dc.description.sponsorshipTUBITAK [215E324]en_US
dc.description.sponsorshipThis work is funded by TUBITAK Grant 215E324en_US
dc.identifier.citation1
dc.identifier.doi10.1109/SIU53274.2021.9478009en_US
dc.identifier.isbn978-1-6654-3649-6
dc.identifier.scopus2-s2.0-85111470200en_US
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9478009
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5233
dc.identifier.wosWOS:000808100700250en_US
dc.identifier.wosqualityN/A
dc.khas20231019-WoSen_US
dc.language.isotren_US
dc.publisherIEEEen_US
dc.relation.ispartof29th Ieee Conference on Signal Processing and Communications Applications (Siu 2021)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectChannel parameters estimationen_US
dc.subjectdeep CNNsen_US
dc.subjectimage classificationen_US
dc.titleAnalysis of deep learning based path loss prediction from satellite imagesen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
relation.isAuthorOfPublicationab26f923-9923-42a2-b21e-2dd862cd92be
relation.isAuthorOfPublication.latestForDiscoveryab26f923-9923-42a2-b21e-2dd862cd92be

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