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ABSTRACT

 

One of the most important requirements to be addressed by a general purpose Fault Management
(FM) system is the ability to quickly identify the root cause of network errors and fix them as
soon as possible. This informs the maintenance of an accurate model of the mobile network error
logs for the FM task and the faults dynamic feature trend/statistical analysis of the error-log
message summary for both the Base Station Subsystems and Mobile Switching Centers by
comparing the relationship between the twin parameters of Frequency of Occurrence and the
Mean Time to Repair each fault type. The Fault Trend Analysis shows that as the numbers of
times a particular fault type occur increases the Mean Time to Repair such faults decreases, with
some deviation from this trend in some cases when we classified them as high priority faults with
serious impact on network performance and those that have little or no impact on network
performance. Filtering and Correlation are two methods we used to simplify the separation of the
principal alarms and redundant alarms from their side effect on network performance. These
algorithms are also presented: Bayesian Inference Technique and Intelligent Management
Information Base (I-MIB) MATLAB Simulation, which enabled us to systematically combine
the qualitative and quantitative aspects of the Bayesian model for network fault management
analysis and to reduce total computational complexity by providing a database of software alarm
parameters respectively. These resulted in a Global Bayesian Network which helps to represent
causal chains, i.e. links between cause/effect relationships to provide the evidence of past events
and show the most likely future causes and their symptoms by computing Conditional
Probabilities of each Symptoms given a particular cause i.e. using the expression P(S= Si,j / C=
Ck ) to populate the Conditional Probability Table. This is critical and useful for effective Global
Systems for Mobile Communications/General Packet Radio Service (GSM/GPRS) Fault
Management Systems and could be deployed by Service providers for quick and proper
diagnosis/troubleshooting of network event/alarm error-logs to help reduce the total downtime
and improved quality of service.

 

TABLE OF CONTENTS

Cover page…………………………………………………………………………..…………. i
Declaration………………………………………………………………………………….…. ii
Certification …………………………………….………………………………………… . . iii
Dedication……………………………..………………………………….……………….….. iv
Acknowledgements..………………………………………………………………….…….……v
Abstract…………………………………………………………………………………….……vi
Table of Contents .…………………………………………………….……………………… vii
List of Tables …..………………………….……………………….…………………….………x
List of Figures..…..……………………………………………..………………………..…….. xiv
List of Appendices ………………………………………………………………………… xix
List of Symbols and Abbreviations…………………………………………………..……..xx
Chapter One ……………………………………………………………………………………………………….1
Introduction……………………..……………………………………………………….……1
1.1
Preamble……………………………………………………………………………………….1
1.2 Thesis Motivation ……………………………………………………………………………………………………4
1.3Statement of Problem ………………………………………..……….……………….…..5
1.4 Thesis Outline……………………………………………………………………………..6
Chapter Two…………………………………………………………………………………….7
Literature Review and Theoretical ackground………………………………….……….…….7
2.1 Literature Review……………………………………………………………………………………………………7
2.2 International Telecommunications Union FCAPS and OSS Framework………………… 10
2.2.1 Fault Management………………………………..………………………….……….14
2.2.2. Sof tware Management ……………………………………..………………………..15
2.3 Fault Diagnosis System Based on Bayesian Networks .……….……….………………17
2.3.1 Bayes’ Theorem …………….……………………………………..…………….….. 18
2.3.2 Bayesian Networks ……………………………………………….……………….…19
2.4 Software Reliability Concept …………………………..…………………………….. 21
2.5 Software Alarm Filtering and Correlation Technique ..….……………………..…….24
2.5.1Rule-Based (Model-Based) System…………….…….…….……………….………….25
2.5.2 Codebook System……………………………………………………..….………….…26
2.5.3 Artificial Intelligence Systems……………………………………………….……………………..27
2.6 Integrated Diagnostic Techniques Based on Neuro-Fuzzy Inference AI System………28
Chapter Three….……………………………….……………………………………………….31
Design Methodology and Data Collection …………………..……………..………….…….31
3.1 Introduction…………………………………….…………………………………………….31
3.2 Methodology ……………………………………………………………………….….…31
3.3 Data Collection………………………………………………………………….………..32
3.4 Presentation of Derived Parameters (Data)…………………………………………………………………33
ChapterFour………………………………………………………………………………………………………………….35
Results and Analysis……………………………………………………………………………………………………….35
4.1 Introduction……………………………………………………………………………………………………………..35
4.2 Fault Trend Analysis of Network Elements Error-Log
Message Summary………………………………………………………………………………………………………….35
4.2.1Fault Trend Analysis of Base Station Subsystems (BSS) Error-Log message…………………..36
4.2.2 Fault Trend Analysis of Mobile Station Center (MSC) Error-Log Message Summar…..38
4.2.2.1Interpretation of Results for MSC………………………………………………………………………..39
4.3 Bayesian Modelling of BSS and MSC Derived Error-Log Message Summary………………40
4.3.1 Qualitative Modeling of Network Elements Error-Log Message Summary Using Simple
Bayesian Model (SBM) (First phase)………………………………………………………………………….41
4.3.2 Quantitative Modeling of Network Elements Error-Log Message Summary Using
Bayesian Modelling (BM) (Second Phase)………………………………………………………………..43
4.3.2.1 Interpretation of Results…………………………………………………………………………………46
4.4 Intelligent Management Information Base (IMIB) MATLAB Simulation for
Bayesian Inference Using Visual Querry Builder GUI Database Tool (Proposed)……….47
4.4.1 Setting Up a Data Source to Work With the VQB………………………………………………48
4.4.2 Interpretation of the Simulated Result……………………………………………………………..50
ChapterFive………………………………………………………………………………………………………….54
Conclusions and Suggestions for Further Work………………………………………………………..54
5.1 Significance of the Study…………………………………………………………………………………..54
5.2 Limitations……………………………………………………………………………………………………..55
5.3 Summary………………………………………………………………………………………………………..56
5.4 Conclusions…………………………………………………………………………………………………….57
5.5 Recommendations for Further Work…………………………………………………………………58
References…………………………………………………………………………………………………………60
Appendices…………………………………………………………………………………………………………66

 

 

CHAPTER ONE

INTRODUCTION
1.1 Preamble
The Second Generation Global System for Mobile communications (2G GSM) was
designed primarily to provide circuit switched speech and data value added services were
simply based on the Short Message Services (SMS). Today, most GSM network
Operators are experienced in a voice oriented Time Division Multiplexed (TDM)
technologies and have gone further to introduce a more complex Third Generation (3G)
networks which is based on the Code Division Multiplexed (CDM) technologies with its
other flavors, e. g. Wideband Code Division Multiple Access (WCDMA), Asynchronous
Transport Module (ATM) in the transport layer for the Universal Mobile
Telecommunications Systems (UMTS), (Maye and Morvan, 2006; Nicolas and Pierce-
Nenri, 2003).
Following development of the internet, the General Packet Radio Services (GPRS) was
introduced to handle packet data services in GSM network. An additional Internet
Protocol (IP) network infrastructure was introduced along with new services platforms,
e.g. the Multimedia Messaging Services (MMS). Information Technology components
such as Network Management System (NMS) and Operations Support Systems (OSS)
needs to be enhanced to be able to properly monitor and manage GSM network elements
end-to-end (Nicolas and Pierce-Nenri, 2003). Hence, network monitoring tools are
sufficiently implemented in network operation and maintenance.
This research work seeks to explore the implementation of the operations and
maintenance functionalities called the Operations Support Systems of Airtel Networks
and possible areas of improvement. The OSS is the functional entity from where network
operators monitor and control the entire Mobile systems.
The complex environment in a GSM/UMTS networks has made it imperative for Service
Providers to ensure more resilient network architecture and the use of object oriented
software e.g. C++, java and Relational SQL Database (Hughes, 2006) for specific tasks
implemented in the OSS with minimal errors expected in the algorithm has also become
important. But, network operations are usually bedeviled with all sorts of faults and
network errors despite the advancement.
To help mobile service providers meet these new challenges with a better quality of
services and to facilitate the smooth integration of multi-technology; the OSS has evolved
into a most important element in a typical GSM network (Dereviere and Verbaus, 2003).
Hence, the need to ensure that network monitoring tool is provided across all network
elements.
The OSS have been designed as an open solution that helps mobile service providers
meet the challenges and analyze how network faults could impact negatively on the
network performance. There are many different reference models, technologies, system
tools to cover the various functions of GSM network management. In terms of the
reference models, the most well known model include the International Standards
Organization Fault, Configuration, Accounting, Performance and Security (ISO FCAPS)
management systems; ITU Proposed the model called Telecommunications Management
Network (TMN). The newer ones proposed by the Tele- management forum is called
Telecoms Operations Map (TOM) and enhanced Telecoms Operations Map (e- TOM)
(Paessler, 2007; Wendel, 2009).
This Work focuses on FCAPS model which is overlapping with the most popular
traditional models deployed by any service provider called Operations, Maintenance and
Provisioning (OAM&P).
There are many network management technologies and protocols which addresses some
of the network management functions, the most popular being deployed is Transmission
Control Protocol Internet Protocol (TCP/IP), Simple Network Management Protocol
(SNMP) defined by IETF and Common Management Information Protocol/Service
(CMIPs) defined by ISO.
As a prerequisite for ensuring better network operations, the OSS which comprises of the
NMC and OAM&P systems is made possible with the various software implementations
across all GSM Network Elements (NE). It is important to state here that Network
operators deploy equipments from different manufacturers as they are all based on the
same open standards, but one major disparity between them is that each has its own
specialized software features for managing and monitoring such equipment.
1.2 Thesis Motivation
The OSS has been designed as an open solution that helps mobile service providers meets
the challenges of service delivery and the desired Quality of Service. It is also important
to realize that customers are willing to pay for new services that meet their needs which
are delivered with perfect quality and as per Service Level Agreement (SLA).
Naturally, the mobile service provider will rely on NMS (OSS) systems to provide realtime
fault, performance, and configuration management for their network. As new
services are introduced by network providers and network expand; the traffic load (and or
processor load) will increase, hence, the need to have their network management systems
grow to accommodate these new services platform for effective end-to-end network
management and event monitoring through the implementation of an effective Software
Management System. The emerging services being introduced in Nigeria by Mobile
Service Providers (MSP) are not yet being managed and monitored properly.
This research work will investigate the Airtel’s network performance in this regard and
explore the different techniques of services provisioning and holistic approach to
integrated network management systems for a better and more robust network operation.
The desire to have GSM network operations devoid of longer downtime due to network
errors; be it hardware or software, and faults detected as fast as possible before they cause
potential service disruption in the form of degradation of performance and loss of
revenue (increased COPEX) in mobile services provisioning and network operation is the
singular most important drive of this work.
Although the field of state-of-art integrated Network Software Management Systems is
relatively new in research community, efficient and effective Network and Element
Management Systems which forms the heart of a Mobile Network Operations Center for
network monitoring on all levels needs thorough consideration.
1.3 Statement of Problem
The real time nature of today’s mobile telecommunications networks adds to the
difficulty of the fault management systems e.g. a diagnostic system must be able to
handle alarm notification flow as soon as they are generated. The maintenance of an
accurate model of an integrated mobile network configuration is critical for the fault
management system. However, efficient tests including software management needs to be
performed on a regular basis to ascertain the true state of Network’s Quality of Service
(QOS).
The Network Management systems information gathering tools needs to be partially (not
wholly) automated and their results consistently interpreted to help the diagnosis and
decision process after proper analysis of complex Software fault management data and
error logs. Software faults are an integral part of the world of wireless (GSM) technology
due to so many reasons: Software patches and code errors i.e. wrong parameterization of
software, Interference, program execution errors and Mean Time to Failure (MTTF)
including but restricted to Mean Time to Repair. Given the complexity and size of
network Software alarms, filtering and correlating the large volumes of data (Error-logs
messages) and choosing the correct tool/technique from the large set of tools available to
carry out the Fault trend analysis requires great effort.
The alarm represents the symptoms and as such there are two real world concerns: The
sheer volume of alarm events as network software errors are logged and modelling of the
causes/effects of fault types. There are no software that simulates the Cause/Effect
elements of Network events error-log, Hence, an accurate model based on both
Structural and empirical parameters needs to be used to analyse the summary of network
error-log messages. Also, using derived error-log message summary of a peculiar data
from a live network to validate the Bayesian Inference technique proposition: Posterior x
Evidence = Likelihood x Prior, getting posterior probability is always difficult. Thus,
resolving the Likelihood and the prior probabilities is far more easier but rarely
undertaken for Fault Management task; either as a result of the difficulty in getting data
or unavailabilty of database of the previous error-log messages.
1.4 Thesis Outline
The thesis is made up of five chapters. Chapter one is Introduction which comprises of:
Preamble, Thesis Motivation, Statement of Problem, and Thesis Outline. Chapter Two is
Literature Review and Theoretical Background. Chapter Three consists of Design
Methodology and Data collection. Results and discussion formed Chapter Four while
Chapter Five covers Conclusions and Suggestions for Further Work.

 

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