RT Conference Proceedings
T1 Modelado de rendimiento de segmento en redes de acceso radio mediante aprendizaje supervisado
A1 Gijón-Martín, Carolina
A1 Toril-Genovés, Matías
A1 Luna-Ramírez, Salvador
A1 Bejarano-Luque, Juan Luis
K1 Redes de banda ancha - Congresos
AB In 5G systems, the Network Slicing (NS) feature allows to deploy several logical networks customized for specific verticals over a common physical infrastructure. In the Radio Access Network (RAN), cellular operators need slice performance models for re-dimensioning purposes. In this work, we present a comprehensive analysis assessing the performance of Supervised Learning (SL) to estimate slice throughput in the down link of RAN-sliced networks, relying on information collected in the operations support system. Different SL algorithms are tested in two NS scenarios with single-service and multi-service slices, respectively. To this end, synthetic datasets with performance indicators and connection traces are generated with a systemlevel simulator emulating the activity of a sliced RAN in a live scenario. Results show that the best model (i.e., combination of SL algorithm and input features) may vary depending on the NS scenario. The best models have shown an error below 10 %.
YR 2022
FD 2022-09
LK https://hdl.handle.net/10630/24993
UL https://hdl.handle.net/10630/24993
LA spa
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026