Coverage Area Estimation Using a Multi-Branch 1D Convolutional Neural Network

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dc.contributor.authorUğur, Erbaş
dc.contributor.authorBekiryazıcı, Tahir
dc.contributor.authorAydemır, Gürkan
dc.contributor.authorTabakcıoğlu, Mehmet Barış
dc.date.accessioned2026-02-08T15:08:27Z
dc.date.available2026-02-08T15:08:27Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractCoverage estimation in cellular networks is crucial for network planning and optimization. Although traditional ray tracing models radio wave propagation, it faces limitations in large-scale applications due to high computational costs. Deep learning-based methods also estimate signal propagation accurately but are restricted by data requirements. To address this issue, this study generates large-scale synthetic datasets using Uniform Theory of Diffraction (UTD)- based ray tracing simulations. The proposed method analyzes electromagnetic propagation paths from 3D digital terrain maps to produce 2D coverage maps and uses a \"Multi-Branch Coverage Estimation Network\" to predict signal propagation quickly and accurately. The model can analyze an area with a 1 km diameter in 80 seconds, offering a significant speed advantage over conventional methods. The RMSE remains below 0.07 dB for all points and below 0.03 dB for 95% of them. In this way, high accuracy in wireless network planning can be achieved without the need for actual measurements.
dc.identifier.doi10.46387/bjesr.1661104
dc.identifier.endpage147
dc.identifier.issn2687-4415
dc.identifier.issue2
dc.identifier.startpage135
dc.identifier.trdizinid1354935
dc.identifier.urihttps://doi.org/10.46387/bjesr.1661104
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5042
dc.identifier.volume7
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMühendislik bilimleri ve araştırmaları dergisi (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR-Dizin_20260207
dc.subjectCoverage Area
dc.subjectDeep Convolutional Neural Networks
dc.subjectGeometric Optic
dc.subjectUniform Theory of Diffraction
dc.titleCoverage Area Estimation Using a Multi-Branch 1D Convolutional Neural Network
dc.typeArticle

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