Predicting path loss distributions of a wireless communication system for multiple base station altitudes from satellite images

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dc.authorid0000-0003-0779-9620
dc.authorid0000-0002-6842-1528
dc.contributor.authorShoer, İbrahim
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorBaykaş, Tunçer
dc.date.accessioned2023-02-03T11:55:17Z
dc.date.available2023-02-03T11:55:17Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractIt is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.
dc.identifier.citationShoer, İ., Güntürk, B. K., Ateş, H. F. ve Baykaş, T. (2022). Predicting path loss distributions of a wireless communication system for multiple base station altitudes from satellite images. IEEE International Conference on Image Processing (ICIP) içinde (2471-2475. ss.). Bordeaux, 16-19 October 2022. https://dx.doi.org/10.1109/ICIP46576.2022.9897467
dc.identifier.doi10.1109/ICIP46576.2022.9897467
dc.identifier.endpage2475
dc.identifier.isbn9781665496209
dc.identifier.issn1522-4880
dc.identifier.scopus2-s2.0-85146729305
dc.identifier.scopusqualityN/A
dc.identifier.startpage2471
dc.identifier.urihttps://dx.doi.org/10.1109/ICIP46576.2022.9897467
dc.identifier.urihttps://hdl.handle.net/20.500.12511/10402
dc.identifier.wos001058109502113en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGüntürk, Bahadır Kürşat
dc.institutionauthorAteş, Hasan Fehmi
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.ispartofIEEE International Conference on Image Processing (ICIP)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/215E324
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectConvolutional Neural Networks
dc.subjectDeep Learning
dc.subjectPath Loss Estimation
dc.subjectUAV Networks
dc.titlePredicting path loss distributions of a wireless communication system for multiple base station altitudes from satellite images
dc.typeConference Object

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