dc.contributor.author | Ahmadien, Omar | |
dc.contributor.author | Ateş, Hasan Fehmi | |
dc.contributor.author | Baykaş, Tunçer | |
dc.contributor.author | Güntürk, Bahadır Kürşat | |
dc.date.accessioned | 2020-08-13T08:51:35Z | |
dc.date.available | 2020-08-13T08:51:35Z | |
dc.date.issued | 2020 | en_US |
dc.identifier.citation | Ahmadien, 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.2020.2985929 | en_US |
dc.identifier.issn | 2169-3536 | |
dc.identifier.uri | https://dx.doi.org/10.1109/ACCESS.2020.2985929 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/5718 | |
dc.description.abstract | Path 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.iso | eng | en_US |
dc.publisher | IEEE - Institute of Electrical and Electronics Engineers, Inc. | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Solid Modeling | en_US |
dc.subject | Three-Dimensional Displays | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Computational Modeling | en_US |
dc.subject | Satellites | en_US |
dc.subject | Buildings | en_US |
dc.subject | Transmitters | en_US |
dc.subject | Path Loss | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Convolutional Neural Networks | en_US |
dc.title | Predicting path loss distribution of an area from satellite ımages using deep learning | en_US |
dc.type | article | en_US |
dc.relation.ispartof | IEEE Access | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | 0000-0002-6842-1528 | en_US |
dc.authorid | 0000-0001-9535-2102 | en_US |
dc.authorid | 0000-0003-0779-9620 | en_US |
dc.identifier.volume | 8 | en_US |
dc.identifier.startpage | 64982 | en_US |
dc.identifier.endpage | 64991 | en_US |
dc.relation.tubitak | info:eu-repo/grantAgreement/TUBITAK/SOBAG/215E324 | |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.doi | 10.1109/ACCESS.2020.2985929 | en_US |
dc.identifier.wosquality | Q2 | en_US |
dc.identifier.scopusquality | Q1 | en_US |