PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES

dc.authorscopusid 57194181977
dc.authorscopusid 6602111220
dc.authorscopusid 7003483541
dc.authorscopusid 24328990900
dc.contributor.author Shoer, I.
dc.contributor.author Gunturk, B.K.
dc.contributor.author Ates, H.F.
dc.contributor.author Baykas, T.
dc.date.accessioned 2023-10-19T15:05:19Z
dc.date.available 2023-10-19T15:05:19Z
dc.date.issued 2022
dc.department-temp Shoer, I., Koç University, Electrical Engineering Department, Turkey; Gunturk, B.K., Istanbul Medipol University, School of Engineering and Natural Sciences, Turkey; Ates, H.F., Istanbul Medipol University, School of Engineering and Natural Sciences, Turkey; Baykas, T., Kadir Has University, Electrical Engineering Department, Turkey en_US
dc.description The Institute of Electrical and Electronics Engineers Signal Processing Society en_US
dc.description 29th IEEE International Conference on Image Processing, ICIP 2022 --16 October 2022 through 19 October 2022 -- --185922 en_US
dc.description.abstract It 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. © 2022 IEEE. en_US
dc.description.sponsorship Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 215E324 en_US
dc.description.sponsorship This work was supported by TUBITAK Grant 215E324. sponding author is B.K.Gunturk (bkgunturk@medipol.edu.tr). en_US
dc.identifier.citationcount 2
dc.identifier.doi 10.1109/ICIP46576.2022.9897467 en_US
dc.identifier.endpage 2475 en_US
dc.identifier.isbn 9781665496209
dc.identifier.issn 1522-4880
dc.identifier.scopus 2-s2.0-85146729305 en_US
dc.identifier.scopusquality N/A
dc.identifier.startpage 2471 en_US
dc.identifier.uri https://doi.org/10.1109/ICIP46576.2022.9897467
dc.identifier.uri https://hdl.handle.net/20.500.12469/4822
dc.identifier.wosquality N/A
dc.khas 20231019-Scopus en_US
dc.language.iso en en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartof Proceedings - International Conference on Image Processing, ICIP en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 3
dc.subject convolutional neural networks en_US
dc.subject deep learning en_US
dc.subject path loss estimation en_US
dc.subject UAV networks en_US
dc.subject 3D modeling en_US
dc.subject Antennas en_US
dc.subject Convolutional neural networks en_US
dc.subject Deep neural networks en_US
dc.subject Image segmentation en_US
dc.subject Unmanned aerial vehicles (UAV) en_US
dc.subject Vehicle to vehicle communications en_US
dc.subject Aerial vehicle en_US
dc.subject Convolutional neural network en_US
dc.subject Deep learning en_US
dc.subject Loss distribution en_US
dc.subject Loss estimation en_US
dc.subject Path loss en_US
dc.subject Path loss estimation en_US
dc.subject Satellite images en_US
dc.subject Unmanned aerial vehicle network en_US
dc.subject Vehicle network en_US
dc.subject Base stations en_US
dc.title PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES en_US
dc.type Conference Object en_US
dspace.entity.type Publication

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