Abstract:
In today's telecommunications settings, operators must deal with rapid technological
developments while improving operational efficiency, which is, lowering operational costs
while maximizing network performance. The growing amount of services available and
subscribers has put cellular network service providers under a lot of strain. Subscribers to
cellular networks have varying wants and requirements. Ethio telecom is one of the biggest
telecom service providers in Africa and also the only one in Ethiopia. Its vision is to become a
world-class telecom service provider.
The primary aim of the research in this study is to relook at the concepts of cellular Network
Fault Management proactive measure from a new perspective. The fault happened daily and
need to rectify, so fault management regarding the faults of the cellular network needs proper
attention and urgent solution. Because the company is currently using traditional methods of
Network Management System (NMS), but in this thesis from drive test data, to improve ensure
customer satisfaction, Cellular network fault of cellular must be managed properly. A Viable
Solution to enhance its service available proactively is needed. It is, thus, with this intent that
the researcher is motivated and decided to figure on the problem during this thesis.
So far, most studies on Cellular network fault have focused on existing measurement of data
available. Fault predicts were made by using real Ethio telecom EMS (Element Management
System) data and achieved Previously done fault predicted using Time series considering down
site NN(Neural Network) based and root cause analysis, however, the study had gaps, so the
researcher was motivated to fill the gap.
In this study, the researcher has tried to focus on the issue rather than existing measurement
data from EMS (Element Management System) based to focus on No direct measurement data
available. So improved prediction of Node B network fault empowered drive test. The result
from DT (Drive test) data collected for two months shows that the performance of the Naïve
Bayes with an accuracy of 98%
