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.
