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.
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 wired-in 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, wind-powered, fossil fuel generated (for example “mobile APs” in
vehicles), or something else (Jeffrey,
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
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, 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 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?
(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)
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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