Detecting anomalies in cellular networks using an ensemble method

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 degr...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the 9th International Conference on Network and Service Management (CNSM 2013) pp. 171 - 174
Main Authors Ciocarlie, Gabriela F., Lindqvist, Ulf, Novaczki, Szabolcs, Sanneck, Henning
Format Conference Proceeding
LanguageEnglish
Published 01.10.2013
Subjects
Adaptation models
cell anomaly detection
Computational modeling
Degradation
Key Performance Indicators
performance management
Self-Healing
Self-Organizing Networks (SON)
Support vector machines
Testing
Time series analysis
Training
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2165-9605
DOI:10.1109/CNSM.2013.6727831