Regression of large-scale path loss parameters using deep neural networks

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dc.authorid0000-0002-0151-0067
dc.authorid0000-0002-4566-4551
dc.authorid0000-0002-6842-1528
dc.authorid0000-0003-0779-9620
dc.contributor.authorBal, Mustafa
dc.contributor.authorMarey, Ahmed
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorBaykaş, Tunçer
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.date.accessioned2022-08-18T12:21:57Z
dc.date.available2022-08-18T12:21:57Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractPath loss exponent and shadowing factor are among important wireless channel parameters. These parameters can be estimated using field measurements or ray-tracing simulations, which are costly and time-consuming. In this letter, we take a deep neural network-based approach, which takes either satellite image or height map of a target region as input, and estimates the desired channel parameters. We use the well-known VGG-16 architecture, pretrained on the ImageNet dataset, as the backbone to extract image features, modify it as a regression network to produce channel parameters, and retrain it on our dataset, which consists of satellite image or height map as input and channel parameters as target values. We demonstrate that deep networks can be successfully utilized in estimating path loss exponent and shadowing factor of a region, simply from the region's satellite image or height map. The trained models and test codes are publicly available on a Github page.
dc.identifier.citationBal, M., Marey, A., Ateş, H. F., Baykaş, T. ve Güntürk, B. K. (2022). Regression of large-scale path loss parameters using deep neural networks. IEEE Antennas and Wireless Propagation Letters, 21(8), 1562-1566. http://doi.org/10.1109/LAWP.2022.3174357
dc.identifier.doi10.1109/LAWP.2022.3174357
dc.identifier.endpage1566
dc.identifier.issn1536-1225
dc.identifier.issn1548-5757
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85132524371
dc.identifier.scopusqualityQ1
dc.identifier.startpage1562
dc.identifier.urihttp://doi.org/10.1109/LAWP.2022.3174357
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9656
dc.identifier.volume21
dc.identifier.wos000835774100014en_US
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBal, Mustafa
dc.institutionauthorMarey, Ahmed
dc.institutionauthorAteş, Hasan Fehmi
dc.institutionauthorGüntürk, Bahadır Kürşat
dc.language.isoen
dc.publisherIEEE-Institute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Antennas and Wireless Propagation Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/215E324
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDeep Learning
dc.subjectHeight Map
dc.subjectRegression
dc.subjectWireless Channel Parameter Estimation
dc.titleRegression of large-scale path loss parameters using deep neural networks
dc.typeArticle

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