Predicting Path Loss Distributions of a Wireless Communication System for Multiple Base Station Altitudes From Satellite Images
| dc.contributor.author | Shoer, Ibrahim | |
| dc.contributor.author | Gunturk, Bahadir K. | |
| dc.contributor.author | Ates, Hasan F. | |
| dc.contributor.author | Baykas, Tuncer | |
| dc.contributor.other | Electrical-Electronics Engineering | |
| dc.contributor.other | 05. Faculty of Engineering and Natural Sciences | |
| dc.contributor.other | 01. Kadir Has University | |
| dc.date.accessioned | 2024-10-15T19:39:37Z | |
| dc.date.available | 2024-10-15T19:39:37Z | |
| dc.date.issued | 2022 | |
| 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. | en_US |
| dc.description.sponsorship | TUBITAK [215E324] | en_US |
| dc.description.sponsorship | This work was supported by TUBITAK Grant 215E324. | en_US |
| dc.identifier.citationcount | 0 | |
| dc.identifier.doi | 10.1109/ICIP46576.2022.9897467 | |
| dc.identifier.isbn | 9781665496209 | |
| dc.identifier.issn | 1522-4880 | |
| dc.identifier.uri | https://doi.org/10.1109/ICIP46576.2022.9897467 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12469/6334 | |
| dc.language.iso | en | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | IEEE International Conference on Image Processing (ICIP) -- OCT 16-19, 2022 -- Bordeaux, FRANCE | en_US |
| dc.relation.ispartofseries | IEEE International Conference on Image Processing ICIP | |
| 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.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 | |
| gdc.author.institutional | Baykaş, Tunçer | |
| gdc.author.wosid | Ates, Hasan/M-5160-2013 | |
| gdc.author.wosid | Baykas, Tuncer/Y-8284-2019 | |
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| gdc.description.department | Kadir Has University | en_US |
| gdc.description.departmenttemp | [Shoer, Ibrahim; Gunturk, Bahadir K.; Ates, Hasan F.] Koc Univ, Dept Elect Engn, Istanbul, Turkey; [Shoer, Ibrahim; Gunturk, Bahadir K.; Ates, Hasan F.] Istanbul Medipol Univ, Sch Engn & Nat Sci, Istanbul, Turkey; [Shoer, Ibrahim; Baykas, Tuncer] Kadir Has Univ, Dept Elect Engn, Istanbul, Turkey | en_US |
| gdc.description.endpage | 2475 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.startpage | 2471 | en_US |
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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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