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Detecting anomalies in cellular networks using an ensemble method

  • SRI International
  • Siemens

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

65 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2013 9th International Conference on Network and Service Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013 and SETM 2013
Pages171-174
Number of pages4
DOIs
StatePublished - 2013
Event2013 9th International Conference on Network and Service Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013 and SETM 2013 - Zurich, Switzerland
Duration: 14 Oct 201318 Oct 2013

Publication series

Name2013 9th International Conference on Network and Service Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013 and SETM 2013

Conference

Conference2013 9th International Conference on Network and Service Management, CNSM 2013 and its three collocated Workshops - ICQT 2013, SVM 2013 and SETM 2013
Country/TerritorySwitzerland
CityZurich
Period14/10/1318/10/13

Keywords

  • cell anomaly detection
  • Key Performance Indicators
  • performance management
  • Self-Healing
  • Self-Organizing Networks (SON)

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