ABSTRACT
The work presented in this thesis laid more emphasis on the performance of real time monitoring
and availability of radio signals to a consumer in a communication network. The aim of this study is
to investigate existing method of measuring network availability and to develop a method that can
be applied by service provider as a quality measure for network operations in Nigeria. Attempts
were made to have a real time monitoring of several base transceiver station (BTS) in Nigeria to
ascertain the availability of signal to a subscriber. Most Companies rely on QOS-oriented data
networks such that any form of interrupt can cause considerable economic loses, thus many of these
industries have invested in securing their networks with redundancy and quality of service as well
as demanding high network availability from their network operator. The objective of this
monitoring was to have a common characteristics behavior for all the BTSs as it relates to
performance in real time and subscriber access to the services paid for. In this study, reactive
availability software M2000 mobile management system was used in gathering data from a
ticketing system. From my result in table 4.1-4.12, it was observed that the statistics obtained for
majority of the BTSs for the period of the study shows satisfactory level of network availability,
thus, their availability values was greater than 98% but less than 99.999% of the desired value.
further analysis from the table also reported that some BTSs performed below bar of meeting up to
the desired value of 99.999% availability, however, their availability values fluctuated between 0%
and 62.55%. Thus, the findings also reported the facts that power outages, location of base stations,
poor maintenance and lack of security personnel were mostly responsible for lack of network
availability. Similarly the findings reveal that the factors affecting network availability or access to
network service across Nigeria are the same. This is clear violation of the policy that binds the
service providers and the subscriber.
viii
LIST OF FIGURES
Figure 2.1: Point-to-point availability of four units 12
Figure 2.2: Serial and parallel availability 12
Figure 2.3: Illustration of the UAS definition 23
Figure 2.4: Relationship between availability, MTBF, and MTTR 27
Figure 3.1: M2000 client interface (Physical Topology window) 32
Figure 3.2: Screen shot from M2000 mobile monitoring system showing BTS
down under their various BSC 33
Figure 3.3: Screen shot from ACC monitoring system for creating trouble ticket
(TT) for the various alarms on the BTS 33
Figure 4.1: Availability statistics of BTS in the Month of January, 2015 93
Figure 4.2: Availability statistics of BTS in the Month of February, 2015 94
Figure 4.3: Availability statistics of BTS in the Month of March, 2015 95
Figure 4.4: Availability statistics of BTS in the Month of April, 2015. 95
Figure 4.5: Availability statistics of BTS in the Month of May, 2015 96
Figure 4.6: Presents the availability statistics and trend for the month of June 97
Figure 4.7: Availability statistics of BTS in the Month of July, 2015 97
Figure 4.8: Availability statistics of BTS in the Month of August, 2015 98
Figure 4.9: Presents the availability statistics and trend for the Month of September 99
Figure 4.10: Availability statistics of BTS in the Month of October, 2015. 99
Figure 4.11: Availability statistics of BTS in the Month of November, 2015 100
Figure 4.12: Availability statistics of BTS in the Month of December, 2015 100
Figure 4.13: Shows the statistics of best performing cells of BTS in the Month
of January, 2015. 101
ix
Figure 4.14: Statistics of worst performing cells in the month of January, 2015 102
Figure 4.15: Shows the statistics of worst performing cells of BTS in the Month
of February, 2015 103
Figure 4.16: Shows the statistics of best performing cells of BTS in the
Month of February, 2015 104
Figure 4.17: Shows the statistics of worst performing cells of BTS in the
Month of March 105
Figure 4.18: Shows the statistics of best performing cells of BTS in the
Month of March, 2015 106
Figure 4.19: Shows the statistics of worst performing cells of BTS in the
Month of April, 2015 107
Figure 4.20: Shows the statistics of best performing cells of BTS in the
Month of April, 2015 108
Figure 4.21: presents the availability statistics for the worst performing cells
of BTS in the month of May, 2015. 108
Figure 4.22: Shows the statistics of best performing cells of BTS in the month of
May, 2015 109
Figure 4.23: Shows the statistics of worst performing cells of BTS in the
Month of May, 2015 110
Figure 4.24: Shows the statistics of best performing cells of BTS in the
Month of June, 111
Figure 4.25: Statistics of worst performing cells of BTS in the month
of July, 2015 111
Figure 4.26: Shows the statistics of best performing cells of BTS in the
Month of July, 2015 112
x
Figure 4.27: Statistics of worst performing cells of BTS in the month of August, 2015 113
Figure 4.28: Shows the statistics of best performing cells of BTS in the month of
August, 2015 113
Figure 4.29: Statistics of worst performing cells in the month of September, 2015 114
Figure 4.30: Shows the statistics of best performing cells of BTS in the month of
September, 2015 114
Figure 4.31: Statistics of worst performing cells of BTS in the month of October, 2015 115
Figure 4.32: Shows the statistics of best performing cells of BTS in the
month of October, 2015 116
Figure 4.33: Shows the statistics of best performing cells of BTS in the month
of November, 2015 116
Figure 4.34: Statistics of worst performing cells of BTS in the month of
November, 2015 117
Figure 4.35: Statistics of worst performing cells of BTS in the month
of December, 2015 117
Figure 4.36: Shows the statistics of best performing cells of BTS in the month
of December, 2015 118
Figure 4.37: Shows the contribution of the Causes of network Outages in the
month of January to December, 2015 119
xi
LIST OF ABREVIATIONS
ACC Application Control Centre
BTS Base Transceivers Station
BS Base Station
CDMA Code Division Multiple Access
FSE Field Support Engineer
GSM Global System for Mobile Communication
ITU International Telecommunication Union
ITIL Information Technology Infrastructure Library
MTTR Mean Time To Repair
MTBF Mean Time between Failures
RAN Radio Access Network
RX Receiver
RAB Radio Access Bearer
RTM Real time Monitoring
RNC Radio Network Controller
STCO Short Time Cell Outages
SLA Service Level Agreement
TT Trouble Ticket
UAS Unavailable Seconds
UMTS Universal Mobile Telecommunication System
3G 3
rd Generation
2G 2
nd Generation
WCDMA Wideband Code Division Multiple Access
xii
LIST OF TABLES
Table 2:1: Availability percentage in minute 11
Table 2.2: Measuring Availability in Telecommunication Network 16
Table 2.3: Error performance objectives for PDH and SDH radio links
belonging to the Access network section of the national
portion of the HRP according to ITU-T G.826 23
Table 2.4: Error performance objectives for SDH radio links belonging to the
Access network section of the national portion of the HRP according
to ITU-T G.828 24
Table 3.1: Network Operators and Their Frequency Bands 30
Table 3.2: Description of Location 30
Table 3.4: Daily Measured Mean time to repair (MTTR) For
The individual site in the Month of January, 2015 35
Table 3.5: Daily measured Mean time to repair (MTTR) for the Individual Site
in the Month of February, 2015 37
Table 3.6: Daily Measured Mean Time To Repair (MTTR) for The Individual
Site in The Month of March, 2015 39
Table 3.7: Daily measured Mean time to repair (MTTR) for the individual
site in the Month of April, 2015 40
Table 3.8: Daily Measured Mean time To Repair (MTTR) for the Individual
Site in the month of May, 2015 43
Table 3.9: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in The Month of June, 2015 45
xiii
Table 3.10: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in The Month of July, 2015 47
Table 3.11: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in The Month of August, 2015 49
Table 3.12: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in the Month of September, 2015 51
Table 3.13: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in the Month of October, 2015 53
Table 3.14: Daily Measured Mean Time To Repair (MTTR) For The
Individual Site in the Month Of November, 2015 55
Table 3.15: Daily Measured Mean Time To Repair (MTTR) For The Individual
Site in the Month of December, 2015 57
Table 3.16: Total Mean Time To Repair (MTTR) For The Month Of
January to December, 2015 58
Table 4.1: Calculated Availability and The Root cause Analysis (RCA) for
The Month of January, 2015 60
Table 4.2: Calculated Availability and The Root cause Analysis (RCA) For
The Month of February, 2015 62
Table 4.3: Calculated Availability and the Root cause Analysis (RCA) For
The Month of March, 2015 64
Table 4.4: Calculated Availability and The Root cause Analysis (RCA) For
The Month of April, 2015 66
Table 4.5: Calculated Availability and the Root cause Analysis (RCA) For
The Month of May, 2015 68
xiv
Table 4.6: Calculated Availability and the Root cause Analysis (RCA) For
The Month of June, 2015 70
Table 4.7: Calculated Availability and The Root cause Analysis (RCA) For
The Month of July, 2015 72
Table 4.8: Calculated Availability and the Root cause Analysis (RCA) For
The Month of August, 2015 74
Table 4.9: Calculated Availability and The Root cause Analysis (RCA) For
The Month of September, 2015 76
Table 4.10: Calculated Availability and The Root cause Analysis (RCA) For
The Month of October, 2015 77
Table 4.11: Calculated Availability and the Root cause Analysis (RCA)
For The Month of November, 2015 79
Table 4.12: Calculated Availability and the Root cause Analysis (RCA) For
The Month of December, 2015 80
Table 4.13: The Worst Performing Cells of BTS in the Month of January, 2015 82
Table 4.14: The Worst Performing Cells of BTS in the Month of February, 2015 83
Table 4.15: The Worst performing cells of BTS in the Month of March, 2015 84
Table 4.16: The Worst performing cells of BTS in the Month of April, 2015 84
Table 4.17: The Worst performing cells of BTS in the Month of May, 2015 84
Table 4.18: The Worst performing cells of BTS in the Month of July, 2015 85
Table 4.19: The Worst performing cells of BTS in the Month of August, 2015 85
Table 4.20: The Worst performing cells of BTS in the Month of September, 2015 85
Table 4.21: The Worst performing cells of BTS in the Month of October, 2015 86
Table 4.22: The Worst performing cells of BTS in the Month of November, 2015 86
Table 4.23: The Worst performing cells of BTS in the Month of December, 2015 86
xv
Table 4.24: The Best performing cells of BTS in the Month of January, 2015 87
Table 4.25: The Best performing cells of BTS in the Month of February, 2015 87
Table 4.26: The Best performing cells of BTS in the Month of March, 2015 88
Table 4.27: The Best Performing Cells of BTS in the Month of April, 2015 88
Table 4.28: The Best performing cells of BTS in the Month of May, 2015 89
Table 4.29: The Best performing cells of BTS in the Month of June, 2015 89
Table 4.30: The Best Performing Cells of BTS in the Month of July, 2015 90
Table 4.31: The Best Performing Cells of BTS in the Month of August, 2015 90
Table 4.32: The Best Performing Cells of BTS in the Month of September, 2015 90
Table 4.36: The Best Performing Cells of BTS in the Month of October, 2015 91
Table 4.37: The Best Performing Cells of BTS in the Month of November, 2015 91
Table 4.38: The Best Performing Cells of BTS in the Month of December, 2015 92
Table 4.39: Shows the Statistics of the Root Causes of Outages in the
Month of January-December 92
xvi
CONTENTS
Title Page ii
Declaration iii
Certification iv
Dedication v
Acknowledgement vi
Abstract vii
List of Figures viii
List of Abbreviations xi
List of Tables xii
CHAPTER ONE: INTRODUCTION
1.1 Background to the study 1
1.2. Statement of the Problem 3
1.3. Justification of the Study 3
1.4. Objectives of the Study 6
1.5. Research Methods 6
CHAPTER TWO: LITERATURE REVIEW
2.1. Brief Review of Network Availability 8
2.1.1 The “five-nines” 8
2.1.2. Theoretical Availability 2
2.1.3. Reactive Availability 11
2.1.4. Time Intervals 11
2.1.5. Adjusted availability 11
2.1.6. Customer or Network-management oriented? 12
2.2. What Is Availability? 12
2.3. Measuring Availability 13
2.3.1 The Myth of the Nines 15
xvii
2.4. Brief Review of Radio Access Network (RAN) Architecture. 18
2.5. ITU-T Standard G.826 19
2.5.1 UAS Definition of Availability 20
2.6. Information Technology Infrastructure Library and Service Availability 23
2.6.1 Business Requirement for Availability 24
2.7. Review of Related Works 25
2.8. Network Management Systems (NMS) 26
CHAPTER THREE: MATERIALS AND METHODS
3.1. Introduction 27
3.2: Equipment used 28
3.3. Measurement Procedure 31
3.4. Measurement Conditions 31
3.5. Data analysis/Presentation 31
3.7. Total Mean Time to Repair (MTTR) for the months of January to
December 2015 67
3.7.2: Daily Calculation of Availability for the Individual site 68
CHAPTER FOUR: RESULTS AND DISCUSSION
4.1. Results 69
4.2. Results Discussion 153
CHAPTER 5: CONCLUSION AND RECOMMENDATION
5.1. Conclusion 154
5.2. Contributions to Knowledge 154
5.3. Recommendation 154
REFERENCE
1
CHAPTER ONE
INTRODUCTION
1.1 Background to the study
The global cellular communication network is one of electrical engineering’s crowning
achievements, reliably connecting over half the planet’s population, virtually everywhere where
people are. These networks – particularly in urban areas are in the midst of a paradigm shift as the
number of base stations increases rapidly each year, nearly all by virtue of small base stations (pico
and especially femto) being added to the existing network. This unprecedented escalation is due to
intense consumer demand for faster data connectivity, the impossibility of meeting this demand by
adding spectrum, and the increasing technical and financial viability of small base stations. By
2015, there will be perhaps 50 million base stations (Andrews, 2012) and some even predict that in
the not-too-distant future, say 10-15 years out, the number of base stations may actually exceed the
number of cell phone subscribers (Malladi, 2012), resulting in a cloud-like “data shower” where a
mobile device may connect to multiple base stations, or at least frequently have a base station to
itself (Jefferey, 2012). Base stations are typically envisioned as big, high-power towers or cell sites.
And indeed, many are. Fundamentally though, a base station must do three things. First, it must be
able to initiate and accommodate spontaneous requests for communication channels with mobile
users in its “coverage area”. Second, it must provide a reliable backhaul connection into the core
network. This connection often is but need not be wired, but if wireless (possibly to another wiredin BS) then it must not be in the same spectrum used for communication with the mobile users; else
such a device should be Considered a “relay” Relays may be useful in some cases for coverage
enhancement, but by reusing the same scarce “access” spectrum for backhaul, are inherently
inferior to base stations. And third, base stations need to have a sustainable power source. Usually,
this is a traditional wired power connection but it could in principle be solar, scavenging, windpowered, fossil fuel generated (for example “mobile APs” in vehicles), or something else (Jeffrey,
2
2012). It may seem frivolous to define an ubiquitous technology that has existed for several
decades. But it is important to recognize that traditional tower-mounted base stations – what we will
call macro cells in this paper – are just a single type of base station, albeit the backbone that has
enabled cellular success to date. However, in many important markets, adding further macro cells is
not viable due to cost and the lack of available sites; for example many cities or neighborhood
associations are simply not very cooperative about opening up new tower locations. The problem
facing operators is not coverage – which is now nearly universal – but capacity. There are just too
many mobile users demanding too much data. This will only worsen due to the continuing adoption
of tablets, laptops with cellular connections, and smart phones along with their data-hungry
applications. Adding base stations has been by far the most important factor historically for
increasing capacity. When base stations are added, each user competes with an ever-smaller number
of users for a BS’s bandwidth and backhaul connection: it may even have one or more BSs to itself.
This is the only scalable way to meet the current “capacity crunch”. Note that Wi-Fi access points
typically meet the above three criteria and are thus also BSs by our definition. Wi-Fi is rapidly
integrating with the cellular network and roaming between cellular and Wi-Fi will become
increasing transparent to end users. Smart phones and tablets have sophisticated user interfaces and
high definition screens, expensive rechargeable batteries, substantial consumer software, and
support multiple wireless standards. In short, there is no inescapable reason a BS needs to be more
expensive than the phones they serve, once they have lower transmit power (the power amplifier
cost is considerably higher in BSs, typically). Indeed, as of late 2012, an iPhone costs about 10x
more than a typical Wi-Fi access point. Such trends will soon extend to femto cells and then Pico
cells, in a dramatic reversal from a decade ago, when BSs cost about 1000x compared to the mobile
devices they served (Jeffrey, 2012). A base transceiver station (BTS) is said to be available if it is in
the ON state as a part of the operational policy and has enough energy to serve at least one user, i.e.,
has at least one unit of energy. The probability that a BS of tier k is available is denoted by ⇢k,
3
which may be different for different tiers of BSs due to the differences in the capabilities of the
energy harvesting modules and the load served (Dhillon et al, 2013).
Most companies rely on QOS-oriented data networks such that any form of interrupt can cause
considerable economic losses (Mathias, 2004). As networks grow bigger and more complex, the
factors influencing the network availability increased. Thus, many of these companies have invested
in securing their networks with redundancy and quality of service as well as demanding high
network availability from their network operator. For the network operator measuring and
quantifying the network availability has become an important issue, not only to attract customers,
but also as an indicator of the variation of quality in the network helping to organize maintenance
and expansion of the network (Mathias, 2004). Service availability has become one of the most
important aspects of service delivery in the highly competitive e business economy (Jihong, 2008).
Moreover, service availability has dramatic impact on customer satisfaction and corporate
reputation as customers are just a mouse click away from competitor’s offerings (Fisher, 2000). A
lot of effort and improvement have been made to ensure high availability in each technology
industry (Potegieter et al, 2005).Today most network operators include availability guarantees to
some extent in their service level agreement (SLA) (Mathias, 2004).
1.2: Statement of the Problem
Network outages and service degradations are a fact of life in operating a mobile network. With the
total number of mobile connections now exceeding the world’s human population, this is hardly
surprising. Sometimes, these incidents make it into the public domain. When operators suffer
significant outages that impact a large number of subscribers, this information makes its way into
the media, in no small part due to the rise of social media. When a network or service is down or
delivering poor performance, many of today’s consumers will turn to social networking sites to
share their experiences and vent their frustration. And of course, there are also many smaller-scale
4
outages and service degradations that impact fewer subscribers, or impact them less dramatically,
with the result that they never make it in to the public domain (Patrick, 2016).
Equipment outages in cellular access networks result in degradation or complete service
interruption. Such occurrences cause operator revenue losses and users dissatisfaction (Rao, 2006).
After price and network coverage, outages are now considered the third most significant factor
influencing subscribers churn (Patrick, 2016), what additionally contributes to the revenue losses.
Mostly, mobile operator technical staff detects outages in real time, through reception of auto-mated
failure logs sent to the Operations and Maintenance Center (OMC) (Kyriazakos, 2004). The
Preprint submitted to Computer Communications January 19, 2016 importance of cell outages has
been recognized by the 3rd Generation Partnership Project (3GPP). The 3GPP ongoing work in
developing Technical Specification (TS) 32.541(Release 12), addresses mitigation of cell outages
through automated self-healing process which is part of the next generation Self-Organizing
Network (SON) concept (Markopoulou, 2008). Hence, the analysis presented in this paper can be
useful for future development of cell outage detection and compensation algorithms which are
currently topic of great interest (Li, et al, 2011). The Short-Time Cell Outages (STCO) phenomena
affecting Base Stations (BSs) in a mobile cellular operator network as a short-time outage of all or
some BS cells (sectors) that lasts up to 30 min in a day, thus still guaranteeing more than 98% of
operation. It is a type of outage which cannot be detected directly through an operator network
monitoring system. Although a complete characterization of STCOs has never been reported in the
literature, such events are affecting the cellular network of every mobile operator. In particular, a
statistical analysis of STCOs based on BSs measurements of a complete operator mobile network
has been performed. The results shows that: (i) STCOs impact everyday life of an operator network,
(ii) high load of cells corresponds to an increase in the number of STCOs and their duration, (iii) the
impact of STCOs to single sectors and whole BSs is not negligible, (iv) most of STCOs are
recorded in urban areas compared to rural ones, (v) the impact of STCOs on users is higher in rural
5
areas compared to urban ones, and (vi) the STCOs are correlated with the transferred traffic rather
than the outside air temperature. (Josip et al, 2016).
The research work described above have several limitations as described in the following:
(a) The collection and measurement of data are generally very tedious and costly because large
volume of data needs to be gathered and specialized expensive software also involved.
(b) Monitoring a life network for the purpose of gathering data for this research work can be
very demanding, it involves 24/7 monitoring of all the BTSs of the region under review
with a software (M2000 mobile management system).
1.3. Justification of the Study
Network availability has become one of the most important aspects of service delivery in the highly
competitive e business economy (Jihong, 2008). In recent years, a lot of effort and improvement
have been made to ensure high availability in each technology industry (Potegieter et al.,
2005).Today most network operators include availability guarantees to some extent in their service
level agreement (SLA) (Mattias, 2004). However, the definition of network availability and the
methods of collecting data vary, as there is no standard or praxis commonly used by the network
operators (Mattias, 2004). To bridge this gap, it has become very important to have a well-defined
process for defining and measuring network availability, because having a well-defined process
serves the following purposes:
(a) Maintaining customers service-level agreements
(b) Attracting new customers
(c) Providing statistics for the Network Operations division
With this background already established. The studies will be premeditated on the following
questions identified:
(a) How is Network Availability defined?
(b) How can Network Availability be measured?
6
(c) Why should Network Availability be measured?
(d) What standards of Network Availability exist?
(d) Are there any recommended values for Network Availability parameters?
(e) How can Network Availability measurement be applied to radio access networks in Nigeria?
In this work, we focus on a process that specifically define and measure access to network
availability.
1.4. Objectives of the study
The overall aim of the study is to determine the performance of real time monitoring and network
availability of a radio access signal.
The Specific Objectives of the Study are to:
(a). investigate existing methods for measuring network availability of a Radio access network
(RAN)
(b). develop a method that can be applied by service provider as a quality measure for network
operations in Nigeria.
(c). determine causes of interruption, disturbances and frequent fault in the network.
1.5. Research Methods
The various methods that were adopted in the realization of this study are:-
(a) Measurement of availability data with software called M2000 mobile management system.
This software gives the performance statistics for the network. Its graphic user interface
provides an overview of the network elements and the links connecting them. In this research
work, a particular network provider was put into consideration i.e. (MTN network provider)
7
Below are the outlined steps for data collection.
i) Monitor the alarms on the BTS every second to know when a site is down using the M2000
topology window
(i) Escalate sites down to Field support engineer (FSE) and log a trouble tickets for the site. (The
trouble ticket track the life cycle of the incident with basic information like the start time /date
of the outage)
(ii) Update the ticket and follow up with field support engineer (FSE) and power engineer
assigned to the site, until the site is up and running again.
(iii) Get a detailed root cause analysis of the fault and close the trouble ticket with the end time.
(iv) Pull the files which are in CSV ASCII text file format (i.e. the fields are comma-delimited).
The data will be analysed to know what information can be utilized for the measurement of
availability.
(v) The data will be measure for one year; however, this period will be taken in order to have a
clear picture of the variation of the availability data and in order to have a clear analysis of the
data measured.
(vi) Estimation and analysis of the data collected will be carried out to know the start time, end
time and the assigned time of the outage. These are necessary parameters for calculating
MTTR.
(viii) Using Availability formula, defined as the percentage of time when a system is operational.
This can be calculated using equation (1.1).In section 2 page 10.
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