RT Conference Proceedings
T1 Filtrado de trazas MDT de alta movilidad mediante aprendizaje supervisado
A1 Toril-Genovés, Matías
A1 Gijón-Martín, Carolina
A1 Bejarano-Luque, Juan Luis
A1 Luna-Ramírez, Salvador
A1 Sánchez-Martín, Joaquín
K1 Redes de banda ancha - Congresos
K1 Aprendizaje automático (Inteligencia artificial) - Congresos
AB 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.
YR 2022
FD 2022-09
LK https://hdl.handle.net/10630/24990
UL https://hdl.handle.net/10630/24990
LA spa
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026