2026-05-28T18:30:37Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/198032026-02-03T12:04:56Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Trujillo, José Antonio
author
De la Bandera Cascales, Isabel
author
Palacios, David
author
Barco-Moreno, Raquel
2020-09-18T11:53:05Z
2020-09-18T11:53:05Z
2020-09-18
https://hdl.handle.net/10630/19803
The new 5th generation (5G) mobile networks will bring multiple services and heterogeneous scenarios that will provide large amount of data. In this context, automatic solutions to analyze such amount of data will allow operators to manage nerworks more efficiently. Management actions might be applied in a different way depending on the characteristics of each cell. This paper proposes an automatic framework based on machine learning to analyze and classify cells based on Key Performance Indicators (KPI) from a live network.
spa
Sistemas de comunicaciones móviles
Sistema para la detección y clasificación de patrones de celdas en redes móviles
conference output
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URL
https://riuma.uma.es/bitstreams/965f55c1-6079-4d57-bd8a-fa1458ea01fa/download
File
MD5
ac3da9b22f994fdfdc19bcd9fa1467c7
98322
application/pdf
Resumen_RIUMA_JATS.pdf