Telco

Abstract In the telecommunications domain, as network infrastructure grows more complex, it is becoming increasingly important to develop systems that are able to automate efficient and accurate anomaly detection. Such systems operate on Key Performance Indicators (KPIs) collected from network base stations, and are vital to alert for possible critical operational incidents in a timely manner. These KPIs are most commonly in the form of multivariate time-series, which are challenging to perform anomaly detection on, as anomalous patterns occurs both as correlations between features, as well as temporal windows.

Abstract In the following pages lies our master’s thesis on how the recent advances in deep learning architectures, namely graph neural networks, can perform unsupervised anomaly detection in the Telecommunications (telco) domain. This work is moti- vated by the need for efficient and accurate anomaly detection in the telco domain, where Key Performance Indicators (KPIs) of base stations are continuously being monitored. Furthermore, network infrastructures are constantly being upgraded, 5G is on its way, and there is an exponential increase of devices and antennas.