Online Anomaly Detection System for Mobile Networks

Abstract

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 being currently deployed to increase the frequency reuse. In this way, the number of nodes that collect performance data is being further risen, so the amount of metrics to be managed and analyzed is being highly increased. Therefore, it is fundamental to have tools that automate these tasks and inform the network operator of the relevant information within the vast amount of metrics collected. In this manner, it is particularly important the continuous monitoring of the performance indicators and the automatic detection of anomalies for network operators to prevent the network degradation and users’ complaints. Therefore, in this paper a methodology to detect and track anomalies in the mobile networks performance indicators in real time is proposed. The feasibility of this system is evaluated with several performance metrics and a real LTE-Advanced dataset. In addition, it is also compared with the performance of other state-of-the-art anomaly detection systems.