3G and 4G paging success rate based mobile network anomaly detection using supervised and unsupervised learning

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 the key performance indicators (KPI) or the key quality indicators (KQI). An integral part of mobile network monitoring is it monitor any unusual pattern in the performance data. The pattern detection 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 thesis demonstrates a supervised and unsupervised 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 mobile network anomaly detection. However, implementing supervised machine learning over a large data set is more computational because it requires ground truth determination. On the other hand, unsupervised machine learning will cluster various data volumes without any prerequisite. If proper tuning is in place on this model, it will give an efficient anomaly detection. Another aspect of this thesis is to identify unsupervised machine learning that is best suited for mobile network anomaly detection. To do that a benchmarking approach is performed over three unsupervised machine learning, and these are K-means, DBSCAN, and HDBSCAN. The thumb rule of the benchmark follows as converting the unsupervised machine learning output into a classification problem and then measuring the model performance. The deep learning implication of anomaly detection in 4G network performance data exercise in this thesis and an autoencoder used to see how it performs in anomaly detection with moderate accuracy.