Coverage Area Estimation Based on Convolutional Neural Networks
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In 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.
Açıklama
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEY
Anahtar Kelimeler
coverage area, deep convolutional neural network, geometric optics, UTD
Kaynak
32Nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
