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.
Highlights
A medium-sized telecom network operations centre receives several hundred thousand alarms per day
It does not appear that any significant overfitting has occurred in the experiments and it can be noted that the test error and the validation error have the similar characteristics
The inputs and the outputs of the ANN model were displayed on Google Earth application, so the Base Transceiver Station (BTS) site is represented on the map as a yellow icon and the MW link is represented on the map as a yellow line in case of no alarm and it will be changed to red color in case of existing alarm on the site (BTS) or on the predicated failed link
Summary
A medium-sized telecom network operations centre receives several hundred thousand alarms per day. This volume of alarms creates severe challenges for the operations and management staff [1]. Alarm correlation analysis system is useful method and tool for analyzing alarms and finding the root cause of faults in telecommunication networks [4]. The main objective of this research work is to develop a model which provides the engineers, managers and OMC operators with a decision support system to analyze the alarms and find the root cause of the alarms burst in the GSM network, so that they will be able to make correct decisions, save time during the diagnoses, and provide correct guidance to the maintenance team.
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