PREDICTING PATH LOSS DISTRIBUTIONS OF A WIRELESS COMMUNICATION SYSTEM FOR MULTIPLE BASE STATION ALTITUDES FROM SATELLITE IMAGES
| 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.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 ([email protected]). | en_US |
| dc.identifier.citationcount | 2 | |
| dc.identifier.doi | 10.1109/ICIP46576.2022.9897467 | en_US |
| dc.identifier.isbn | 9781665496209 | |
| dc.identifier.issn | 1522-4880 | |
| dc.identifier.scopus | 2-s2.0-85146729305 | 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.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.rights | info:eu-repo/semantics/closedAccess | en_US |
| 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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| gdc.description.departmenttemp | 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 |
| gdc.description.endpage | 2475 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q3 | |
| gdc.description.startpage | 2471 | en_US |
| gdc.description.wosquality | N/A | |
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| gdc.oaire.keywords | Vehicle network | |
| gdc.oaire.keywords | Aerial vehicle | |
| gdc.oaire.keywords | UAV networks | |
| gdc.oaire.keywords | Base stations | |
| gdc.oaire.keywords | Convolutional neural network | |
| gdc.oaire.keywords | Unmanned aerial vehicles (UAV) | |
| gdc.oaire.keywords | 3D modeling | |
| gdc.oaire.keywords | Deep Learning | |
| gdc.oaire.keywords | Vehicle to vehicle communications | |
| gdc.oaire.keywords | convolutional neural networks | |
| gdc.oaire.keywords | Deep neural networks | |
| gdc.oaire.keywords | Path loss estimation | |
| gdc.oaire.keywords | Satellite images | |
| gdc.oaire.keywords | Path loss | |
| gdc.oaire.keywords | Image segmentation | |
| gdc.oaire.keywords | Convolutional Neural Networks | |
| gdc.oaire.keywords | Loss estimation | |
| gdc.oaire.keywords | Path Loss Estimation | |
| gdc.oaire.keywords | Unmanned aerial vehicle network | |
| gdc.oaire.keywords | deep learning | |
| gdc.oaire.keywords | Deep learning | |
| gdc.oaire.keywords | Loss distribution | |
| gdc.oaire.keywords | path loss estimation | |
| gdc.oaire.keywords | Antennas | |
| gdc.oaire.keywords | Convolutional neural networks | |
| gdc.oaire.keywords | UAV Networks | |
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