Uğur, ErbaşBekiryazıcı, TahirAydemır, GürkanTabakcıoğlu, Mehmet Barış2026-02-082026-02-0820252687-4415https://doi.org/10.46387/bjesr.1661104https://hdl.handle.net/20.500.12885/5042Coverage 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.eninfo:eu-repo/semantics/openAccessCoverage AreaDeep Convolutional Neural NetworksGeometric OpticUniform Theory of DiffractionArticle10.46387/bjesr.1661104721351471354935