Coverage Area Estimation Based on Convolutional Neural Networks

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dc.contributor.authorErba, Ugur
dc.contributor.authorBekiryazici, Tahir
dc.contributor.authorAydemir, Gurkan
dc.contributor.authorTabakcioglu, Mehmet Baris
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
dc.description.abstractIn wireless communication systems, one of the most crucial problems is to solve the problem where to deploy the transmitter. It is essential to determine the coverage area before installing the transmitter. In simulation programs, coverage areas are calculated using ray tracing techniques and numerical integral-based wave propagation models, and positioning is done accordingly. However, when dealing with a large area and obstacles between the transmitter and receiver, the computational load significantly increases. Considering the inadequacy of traditional methods in complex urban environments and rapidly changing conditions, the integration of deep learning techniques is aimed at providing a more accurate and flexible solution. In this context, deep learning models Convolutional Neural Networks (CNNs) based, particularly, are highlighted as a potential solution for base station positioning. Among the advantages of CNNs is their ability to adapt more quickly to complex environmental variables. The developed CNN-based model has shown promising results in coverage area estimation and has the potential to enhance the performance of wireless communication networks. This study aims to contribute to the future reliability, speed, and effectiveness of wireless communication networks.
dc.description.sponsorshipIEEE,IEEE Turkey,Koluman & Berdan,Loodos,Figes,Turkcell,Yildirim Elect
dc.identifier.doi10.1109/SIU61531.2024.10600900
dc.identifier.isbn979-8-3503-8897-8
dc.identifier.isbn979-8-3503-8896-1
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85200916345
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600900
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5908
dc.identifier.wosWOS:001297894700146
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof32Nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectcoverage area
dc.subjectdeep convolutional neural network
dc.subjectgeometric optics
dc.subjectUTD
dc.titleCoverage Area Estimation Based on Convolutional Neural Networks
dc.title.alternativeEvrişimsel Sinir Ağlarına Dayanan Kapsama Alanı Kestirimi
dc.typeConference Object

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