RT Conference Proceedings
T1 Prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis.
A1 García Aguilar, Iván
A1 Galeano-Brajones, Jesús
A1 Luna-Valero, Francisco
A1 Carmona Murillo, Javier
A1 Fernández-Rodríguez, Jose David
A1 Luque-Baena, Rafael Marcos
K1 Redes neuronales (Informática)
K1 Sistemas telefónicos móviles
AB Deploying 5G networks in urban areas is crucial for meetingthe increasing demand for high-speed, low-latency wireless communications. However, the complex topography and diverse building structures in urban environments have challenges in identifying suitable locations for base stations. This research explores leveraging deep learningneural networks to analyze satellite imagery, creating a predictive toolfor identifying potential rooftop locations. Integrating these predictionsinto a user-friendly desktop application simplifies the site selection process, reducing the need for costly and labor-intensive site visits in 5Gnetwork deployment. This approach democratizes the deployment process, making it accessible to a broader audience. The combination ofadvanced technology and satellite imagery offers a promising solution toefficiently deploy 5G base stations in urban landscapes, contributing tothe widespread adoption of this technology in densely populated areasand advancing 5G connectivity globally.
PB Springer
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32259
UL https://hdl.handle.net/10630/32259
LA eng
NO García-Aguilar, I., Galeano-Brajones, J., Luna-Valero, F., Carmona-Murillo, J., Fernández-Rodríguez, J.D., Luque-Baena, R.M. (2024). Prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_4
NO García-Aguilar, I., Galeano-Brajones, J., Luna-Valero, F., Carmona-Murillo, J., Fernández-Rodríguez, J.D., Luque-Baena, R.M. (2024). Prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Bioinspired Systems for Translational Applications: From Robotics to Social Engineering. IWINAC 2024. Lecture Notes in Computer Science, vol 14675. Springer, Cham. https://doi.org/10.1007/978-3-031-61137-7_4
NO Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026