An improved anomaly detection in mobile networks by using incremental time-aware clustering

With the increase of the mobile network complexity, minimizing the level of human intervention in the network management and troubleshooting has become a crucial factor. This paper focuses on enhancing the level of automation in the network management by dynamically learning the mobile network cell states and improving the anomaly detection on the individual cell level taking into consideration not just the multidimensionality of cell performance indicators, but also the sequence of cell states that have been traversed over time. Our evaluation based on the real network data shows very good performance of such a learning model being able to capture the cell behavior in time and multidimensional space. Such knowledge can improve the detection of different types of anomalies in cell functionality and enhance the process of cell failure mitigation.