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dc.contributor.authorAhmadien, Omar
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorBaykaş, Tunçer
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.date.accessioned2020-08-13T08:51:35Z
dc.date.available2020-08-13T08:51:35Z
dc.date.issued2020en_US
dc.identifier.citationAhmadien, O., Ateş, H. F., Baykaş, T. ve Güntürk, B. K. (2020). Predicting path loss distribution of an area from satellite ımages using deep learning. IEEE Access, 8, 64982-64991. https://dx.doi.org/10.1109/ACCESS.2020.2985929en_US
dc.identifier.issn2169-3536
dc.identifier.urihttps://dx.doi.org/10.1109/ACCESS.2020.2985929
dc.identifier.urihttps://hdl.handle.net/20.500.12511/5718
dc.description.abstractPath loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights.en_US
dc.language.isoengen_US
dc.publisherIEEE - Institute of Electrical and Electronics Engineers, Inc.en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectSolid Modelingen_US
dc.subjectThree-Dimensional Displaysen_US
dc.subjectMachine Learningen_US
dc.subjectComputational Modelingen_US
dc.subjectSatellitesen_US
dc.subjectBuildingsen_US
dc.subjectTransmittersen_US
dc.subjectPath Lossen_US
dc.subjectDeep Learningen_US
dc.subjectConvolutional Neural Networksen_US
dc.titlePredicting path loss distribution of an area from satellite ımages using deep learningen_US
dc.typearticleen_US
dc.relation.journalIEEE Accessen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0002-6842-1528en_US
dc.authorid0000-0001-9535-2102en_US
dc.authorid0000-0003-0779-9620en_US
dc.identifier.volume8en_US
dc.identifier.startpage64982en_US
dc.identifier.endpage64991en_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/215E324
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/ACCESS.2020.2985929en_US


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