Unsupervised learning for detection of mobility related anomalies in commercial LTE networks

We propose an unsupervised learning based anomaly detection framework for identifying cells experiencing performance degradation due to mobility problems, in LTE networks. Handover failure rate is used as a performance metric, whereas the mobility problems considered include too-early and too-late handovers. In order to enable unsupervised learning, the framework leverages existing datasets in commercial LTE networks (e.g. performance management counters, configuration management data, geographical locations, and inventory data etc). To this end, the first step is data pre-processing, followed by feature extraction based on principal component analysis and clustering. For implementation, we use real data from an operational commercial LTE network. Results show that clustering is highly effective in understanding and identifying mobility related anomalous behaviour, and provides actionable insights for automation and self-optimization, paving the way for efficient mobility robustness optimization, which is an important self-optimization use-case for contemporary 4G/5G networks.