Estimation of the coverage area with ResNet-based Conditional Variational Autoencoder
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
33rd Conference on Signal Processing and Communications Applications-SIU-Annual -- JUN 25-28, 2025 -- Istanbul, TURKIYE
Anahtar Kelimeler
path loss estimation, base station placement, coverage map, deep learning
Kaynak
2025 33Rd Signal Processing and Communications Applications Conference, Siu
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
