Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks

Abstract

This article presents an anomaly detection (AD) algorithm, robust AD for rural fixed wireless LTE (RAINFOREST), to address the difficulty of fault detection in LTE networks, specifically those that are rural and fixed wireless. We propose a hybrid AD method that uses network key performance indicators (KPIs), historical KPI forecasts, density-based spatial clustering of applications with noise (DBSCAN), and statistical analysis to detect anomalies. RAINFOREST outperformed benchmark AD methods and was able to detect faults in a rural commercial fixed wireless network earlier than existing LTE threshold-based alarms.