Detecting Anomalies in Cellular Networks Using an Ensemble Method

Abstract—The Self-Organizing Networks (SON) concept in-cludes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and com-plete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to deter-mine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance. Index Terms—Self-Organizing Networks (SON), cell anomaly detection, Self-Healing, performance management, Key Perfor-mance Indicator