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Detecting Anomalies in Cellular Networks Using an Ensemble Method
by Dr. Gabriela Ciocarlie, Dr. Ulf Lindqvist, Szabolcs Novaczki & Henning Sanneck.
9th International Conference on Network and Service Management (CNSM), Zurich, Switzerland, 14-18 Oct. 2013, pp. 171-174.
The Self-Organizing Networks (SON) concept includes 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 complete 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 determine 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.
Download published full-text PDF here.