2026-05-31T04:16:00Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/322592026-02-03T12:28:39Zcom_10630_2254col_10630_37959

Prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis. García Aguilar, Iván Galeano-Brajones, Jesús Luna-Valero, Francisco Carmona Murillo, Javier Fernández-Rodríguez, Jose David Luque-Baena, Rafael Marcos Redes neuronales (Informática) Sistemas telefónicos móviles Política de acceso abierto tomada de: https://www.springernature.com/gp/open-research/policies/book-policies Deploying 5G networks in urban areas is crucial for meeting the 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 learning neural networks to analyze satellite imagery, creating a predictive tool for identifying potential rooftop locations. Integrating these predictions into a user-friendly desktop application simplifies the site selection process, reducing the need for costly and labor-intensive site visits in 5G network deployment. This approach democratizes the deployment process, making it accessible to a broader audience. The combination of advanced technology and satellite imagery offers a promising solution to efficiently deploy 5G base stations in urban landscapes, contributing to the widespread adoption of this technology in densely populated areas and advancing 5G connectivity globally. 2024-07-19T11:52:43Z 2024-07-19T11:52:43Z 2024 conference output 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 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 https://hdl.handle.net/10630/32259 10.1007/978-3-031-61137-7_4 eng 10th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2024 Olhâo, Portugal June 4–7, 2024 open access Springer