Online Anomaly Detection System for Mobile Networks
Archivos
Fecha
2020
Profesor GuĆa
Formato del documento
Articulo
ORCID Autor
TĆtulo de la revista
ISSN de la revista
TĆtulo del volumen
Editor
Sensors (Basel)
UbicaciĆ³n
https://doi.org/10.3390/s20247232
ISBN
ISSN
1424-8220
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item.page.doiurl
Facultad
Departamento o Escuela
Determinador
Recolector
Especie
Nota general
Resumen
The arrival of the fifth generation (5G) standard has further accelerated the need for operators to improve the network capacity. With this purpose, mobile network topologies with smaller cells are currently being deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the number of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automatically inform the network operator of the relevant information within the vast amount of metrics collected. The continuous monitoring of the performance indicators and the automatic detection of anomalies is especially important for network operators to prevent the network degradation and user complaints. Therefore, this paper proposes a methodology to detect and track anomalies in the mobile networks performance indicators online, i.e., in real time. The feasibility of this system was evaluated with several performance metrics and a real LTE Advanced dataset. In addition, it was also compared with the performances of other state-of-the-art anomaly detection systems.
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Auspiciador
Palabras clave
LTE, ANOMALY DETECTION, NETWORK OPERATION, SELF-HEALING