RT Conference Proceedings
T1 Time-dependent KPI generation based on Copula
A1 Luo Chen, Hao Qiang
A1 Álvarez-Merino, Carlos Simón
A1 Khatib, Emil Jatib
A1 Barco-Moreno, Raquel
K1 Sistemas de comunicaciones móviles
K1 Aprendizaje automático (Inteligencia artificial)
AB New generations of mobile networks are developed to serve the increasing user and devices connected to the networks. However, the management of these networks has a need of automation, due to the also growing  complexity.  Self-Organizing Network  (SON)  was  conceived  to  fulfil  the  automation  of  network management, within which troubleshooting is located under Self-Healing (SH). The current tendency is the use of Artificial Intelligence (AI) algorithms that are trained using Machine Learning (ML). This training requires a considerable amount of data. Anyway, the reluctance of operators to sharing their data with the  research  community causes a scarcity of data representing degradations that  can  be  used  for  the development and training of ML algorithms. In this paper a method to solve this data sample limitation is proposed.  In  the  first  place,  the method  divides  the  data  into  time  categories  to  create  models which preserve the time characteristics. Afterwards, it applies statistical copulas to adapt the models into new ones maintaining statistical relationships. Finally, the method returns synthetic data that can be an input for ML. As an example, the data from a real mobile network is processed.
YR 2020
FD 2020-09-18
LK https://hdl.handle.net/10630/19801
UL https://hdl.handle.net/10630/19801
LA eng
NO I Plan Propio de Investigación y Transferencia de la Universidad de Málaga
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 1 mar 2026