PL-GAN: Path loss prediction using generative adversarial networks
| dc.authorid | 0000-0002-4566-4551 | |
| dc.authorid | 0000-0002-0151-0067 | |
| dc.authorid | 0000-0002-6842-1528 | |
| dc.authorid | 0000-0003-0779-9620 | |
| dc.contributor.author | Marey, Ahmed | |
| dc.contributor.author | Bal, Mustafa | |
| dc.contributor.author | Ateş, Hasan Fehmi | |
| dc.contributor.author | Güntürk, Bahadır Kürşat | |
| dc.date.accessioned | 2022-09-16T06:29:33Z | |
| dc.date.available | 2022-09-16T06:29:33Z | |
| dc.date.issued | 2022 | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü | |
| dc.department | İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü | |
| dc.description.abstract | Accurate prediction of path loss is essential for the design and optimization of wireless communication networks. Existing path loss prediction methods typically suffer from the trade-off between accuracy and computational efficiency. In this paper, we present a deep learning based approach with clear advantages over the existing ones. The proposed method is based on the Generative Adversarial Network (GAN) technique to predict path loss map of a target area from the satellite image or the height map of the area. The proposed method produces the path loss map of the entire target area in a single inference, with accuracy close to the one produced by ray tracing simulations. The method is tested at 900MHz transmission frequency; the trained model and source codes are publicly available on a Github page. | |
| dc.identifier.citation | Marey, A., Bal, M., Ateş, H. F. ve Güntürk, B. K. (2022). PL-GAN: Path loss prediction using generative adversarial networks. IEEE Access, 10, 90474-90480. https://doi.org/10.1109/ACCESS.2022.3201643 | |
| dc.identifier.doi | 10.1109/ACCESS.2022.3201643 | |
| dc.identifier.endpage | 90480 | |
| dc.identifier.issn | 2169-3536 | |
| dc.identifier.scopus | 2-s2.0-85137576600 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 90474 | |
| dc.identifier.uri | https://doi.org/10.1109/ACCESS.2022.3201643 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/9716 | |
| dc.identifier.volume | 10 | |
| dc.identifier.wos | 000850844600001 | en_US |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Marey, Ahmed | |
| dc.institutionauthor | Bal, Mustafa | |
| dc.institutionauthor | Ateş, Hasan Fehmi | |
| dc.institutionauthor | Güntürk, Bahadır Kürşat | |
| dc.language.iso | en | |
| dc.publisher | IEEE-Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | IEEE Access | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.relation.tubitak | info:eu-repo/grantAgreement/EC/FP7/215E324 | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Deep Learning | |
| dc.subject | Height Maps | |
| dc.subject | Satellite Images | |
| dc.subject | GANS | |
| dc.subject | Channel Parameter Estimation | |
| dc.subject | Wireless Network | |
| dc.subject | Regression | |
| dc.subject | Excess Path Loss | |
| dc.title | PL-GAN: Path loss prediction using generative adversarial networks | |
| dc.type | Article |
