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Journal of Computer Networks and Communications
Volume 2012, Article ID 163184, 13 pages
Research Article

An Approach for Network Outage Detection from Drive-Testing Databases

1Department of Communications Engineering, Tampere University of Technology, 33720 Tampere, Finland
2Department of Mathematical Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland

Received 18 March 2012; Revised 24 September 2012; Accepted 25 September 2012

Academic Editor: Sayandev Mukherjee

Copyright © 2012 Jussi Turkka et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A data-mining framework for analyzing a cellular network drive testing database is described in this paper. The presented method is designed to detect sleeping base stations, network outage, and change of the dominance areas in a cognitive and self-organizing manner. The essence of the method is to find similarities between periodical network measurements and previously known outage data. For this purpose, diffusion maps dimensionality reduction and nearest neighbor data classification methods are utilized. The method is cognitive because it requires training data for the outage detection. In addition, the method is autonomous because it uses minimization of drive testing (MDT) functionality to gather the training and testing data. Motivation of classifying MDT measurement reports to periodical, handover, and outage categories is to detect areas where periodical reports start to become similar to the outage samples. Moreover, these areas are associated with estimated dominance areas to detected sleeping base stations. In the studied verification case, measurement classification results in an increase of the amount of samples which can be used for detection of performance degradations, and consequently, makes the outage detection faster and more reliable.