RT Journal Article
T1 Method for Artificial KPI Generation With Realistic Time-Dependent Behaviour
A1 Luo Chen, Hao Qiang
A1 Álvarez-Merino, Carlos Simón
A1 Khatib, Emil Jatib
A1 Barco-Moreno, Raquel
K1 Sistemas de comunicación móviles
AB Machine Learning (ML) is the dominating solution for the implementation of Self-Organizing Networks (SON), which automate mobile network management. However, the data scarcity derived from the reluctance of operators complicates the necessary training phase ML algorithms. In this letter a method to generate artificial Key Performance Indicators (KPIs) time series is proposed considering their time-dependent behaviour. The data is modelled and categorised according to the time of the day and the data models are adapted with statistical copulas to create samples which present interrelation among different KPIs. Finally, results obtained from a real mobile network are presented.
PB IEEE Xplore
YR 2021
FD 2021-07-07
LK https://hdl.handle.net/10630/37534
UL https://hdl.handle.net/10630/37534
LA eng
NO H. Q. Luo-Chen, C. S. Alvarez-Merino, E. J. Khatib and R. Barco, "Method for Artificial KPI Generation With Realistic Time-Dependent Behaviour," in IEEE Communications Letters, vol. 25, no. 9, pp. 2978-2982, Sept. 2021, doi: 10.1109/LCOMM.2021.3095372. keywords: {Training;Time series analysis;Correlation;Adaptation models;Degradation;Data models;Biological system modeling;KPI modelling;time-awareness;statistical relationship;copula function;correlation},
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026