Table of Contents
Conference Papers in Engineering
Volume 2013, Article ID 958926, 7 pages
http://dx.doi.org/10.1155/2013/958926
Conference Paper

Decision Support System for Alarm Correlation in GSM Networks Based on Artificial Neural Networks

1Mobile Communication Group Department, Alcatel-Lucent, Benghazi, Libya
2Industrial and Manufacturing Systems Engineering Department, University of Benghazi, Benghazi, Libya

Received 27 February 2013; Accepted 9 May 2013

Academic Editors: M. Elmusrati, A. Gaouda, and H. Koivo

This Conference Paper is based on a presentation given by Ashraf Kamal Arhouma at “International Conference on Electrical and Computer Engineering” held from 26 March 2013 to 28 March 2013 in Benghazi, Libya.

Copyright © 2013 Ashraf Kamal Arhouma and Saleh M. Amaitik. 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.

Abstract

As mobile networks grow in size and complexity, huge streams of alarms are flooding the operation and maintenance center (OMC). Thus, the operator needs a decision support system that converts these massive alarms to manageable magnitudes. Alarm correlation is very important in improving the service and the efficiency of the maintenance team in mobile networks and in modern telecommunications networks. As any fault in the mobile network results in a number of alarms, correlating these different alarms and identifying their source are a major problem in fault management. In this paper, an artificial neural network model is proposed to interpret the alarm stream, thereby simplifying the decision-making process and shortening the operator's reaction time. MATLAB program is used as programming tool to develop, implement, and compare between different types of designed artificial neural network models. To assist the operators to take fast decision and detect the root cause of the alarms, the alarms and the result of the artificial neural networks model are visualized in real time on the Google Earth application.