Supervised learning based mobile network anomaly detection from key performance indicator (KPI) data

Abstract

In a mobile network, there are a lot of data that can provide network detail about network efficiency, robustness, and availability. A type of data is mobile network performance data obtained from key performance indicators (KPI) or key quality indicators (KQI). An integral part of mobile network monitoring is it monitor any unusual pattern in the performance data. The unusual pattern or anomaly detection use case from performance data is essential for mobile operators because it detects issues in the network that are not possible to detect by the network alarms. A machine learning-based anomaly detection model is most common nowadays. This paper demonstrates a supervised machine learning-based anomaly detection model. The base data set is paging success rate performance data of day-level and hourly-level granularity. Secondly, a comparative analysis is present over various anomaly detection models. Thirdly, the data used in this paper has an imbalance scenario and how the re-sampling technique can affect the outcome of the anomaly detection model. Lastly, one supervised machine learning recommends for mobile network anomaly detection.