Machine Learning for QoE Prediction and Anomaly Detection in Self-Organizing Mobile Networking Systems

Existing mobile networking systems lack the level of intelligence, scalability, and autonomous adaptability required to optimally enable next-generation networks like 5G and beyond, which are expected to be Self Organizing Networks (SONs). It is anticipated that machine learning (ML) will be instrumental in designing future “x”G SON networks with their demanding Quality of Experience (QoE) requirements. This paper evaluates a methodology that uses supervised machine learning to predict the QoE level of the end user experiences and uses this information to detect anomalous behavior of dysfunctional network nodes (eNodeBs/base stations) in self-organizing mobile networks. An end-to-end network scenario is created using the network simulator ns-3, where end users interact with a remote host that is accessed over the Internet to run the most commonly used applications like file downloads and uploads and the resulting output is used as a dataset to implement ML algorithms for QoE prediction and eNodeB (eNB) anomaly detection. Three ML algorithms were implemented and compared to study their effectiveness and the scalability of the methodology. In the test network, an accuracy score greater than 99% is achieved using the ML algorithms. As suggested by the ns-3 simulation the use of ML for QoE prediction will help network operators understand end-user needs and identify network elements that are failing and need attention and recovery.