Estimation of the coverage area with ResNet-based Conditional Variational Autoencoder

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dc.contributor.authorErbas, Ugur
dc.contributor.authorAvci, Adem
dc.contributor.authorTabakcioglu, Mehmet Baris
dc.date.accessioned2026-02-08T15:15:42Z
dc.date.available2026-02-08T15:15:42Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description33rd Conference on Signal Processing and Communications Applications-SIU-Annual -- JUN 25-28, 2025 -- Istanbul, TURKIYE
dc.description.abstractThe rapid expansion of wireless communication networks, especially with 5G, has made the accurate and fast determination of base station locations critical. With the increase in network traffic and the number of users, it has become important to optimize the network infrastructure. This process is complex, taking into account technical and environmental factors. The study investigates machine learning (ML) and deep learning (DL) techniques to optimize base station placement, emphasizing that traditional methods require manual calculations and field measurements, whereas ML and DL-based approaches are more efficient and faster. Furthermore, a ResNet-based Conditional Variational Autocoder (ResNet-CVO) model for coverage map estimation is proposed and its performance is evaluated. With the proposed generative model, a more efficient approach for coverage map estimation is presented.
dc.description.sponsorshipInstitute of Electrical and Electronics Engineers Inc
dc.identifier.doi10.1109/SIU66497.2025.11111944
dc.identifier.isbn979-8-3315-6656-2
dc.identifier.isbn979-8-3315-6655-5
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-105015557784
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU66497.2025.11111944
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5910
dc.identifier.wosWOS:001575462500108
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIeee
dc.relation.ispartof2025 33Rd Signal Processing and Communications Applications Conference, Siu
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectpath loss estimation
dc.subjectbase station placement
dc.subjectcoverage map
dc.subjectdeep learning
dc.titleEstimation of the coverage area with ResNet-based Conditional Variational Autoencoder
dc.title.alternativeResNet tabanli Ko sullu Varyasyonel Otokoder ile Kapsama Alani Tahmini
dc.typeConference Object

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