2026-05-31T07:22:11Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/249902026-02-03T12:01:16Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Toril-Genovés, Matías
author
Gijón-Martín, Carolina
author
Bejarano-Luque, Juan Luis
author
Luna-Ramírez, Salvador
author
Sánchez-Martín, Joaquín
2022-09-14T10:20:54Z
2022-09-14T10:20:54Z
2022-09
https://hdl.handle.net/10630/24990
In beyond 5G networks, geolocated radio information will play a fundamental role to drive self-management algorithms in a zero-touch paradigm. Minimization of Drive Test (MDT) functionality provides operators with geolocated network performance statistics and radio events. However, MDT traces contain important location errors due to energy saving modes, which requires filtering out wrong samples to guarantee an adequate performance of MDT-driven algorithms. In this context, supervised learning (SL) arises as a promising solution to automate the design of MDT filtering procedures compared to rule-based solutions. This work presents a SL-based method to filter MDT measurements in road scenarios by combining user mobility traces and land use maps in the absence of labeled real user mobility traces. Assessment is carried out over real MDT data collected in a live LTE network. Results show that the model performs better in measurements with positioning accuracy information.
spa
Redes de banda ancha - Congresos
Aprendizaje automático (Inteligencia artificial) - Congresos
Filtrado de trazas MDT de alta movilidad mediante aprendizaje supervisado
conference output
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URL
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File
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4c2110b44c1ed0742a1d6321c60f4eff
9505
application/pdf
documento_riuma_jmsm.pdf