A practical optimisation method to

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 6, No. 5, October 2014
A PRACTICAL OPTIMISATION METHOD TO
IMPROVE QOS AND GOS-BASED KEY
PERFORMANCE INDICATORS IN GSM
NETWORK CELL CLUSTER ENVIRONMENT
Joseph Isabona1 and Kingsley Obahiagbon2
1Department of Basic Sciences Benson Idahosa University, PMB.1100, Benin City,
Nigeria
2Department of Mathematics and Computer Sciences, Idahosa University, PMB.1100,
Benin City, Nigeria
ABSTRACT
The delivering of both good quality of service (QoS) and Grade of Service (GoS) in any competitive mobile
communication environment is a major factor to reducing subscribers’ churn rate. Therefore, it is
important for wireless mobile network operators to ensure stability and efficiency by delivering a
consistent, reliable and high-quality end user (subscriber) satisfaction. This can only be achieve by
conducting a regular network performance monitoring and optimisation as it directly impacts the quality of
the offered services and hence user satisfaction. In this paper, we present the results of network
performance evaluation and optimisation of a GSM network on cell cluster-basis, in Asaba region, South
East Nigeria. We employ a combination of essential key performance indicators such as dropped call rate,
call setup success rate and outage call rate to examine overall QoS and GoS performance of the GSM
network. Our results after network optimisation showed significant performance improvement in terms of
call drop rate, call set up success rate, and call block rate across. Specifically, the end user satisfaction
rate has increased from 94.45%, 87.74%, and 92.85% to 99.05%, 95.38% and 99.03% respectively across
the three GSM cell clusters. The GoS is reduced from 3.33%, 6.60% and 2.38% to 0.00%, 3.70% and
0.00% respectively. Furthermore, ESA, which correspond end points service availability, has improved
from 94.44%, 93.40% and 97.62% to 100%, 96.30% and 100% respectively. In addition, the average
throughput has improved from 73.74kbits/s, 85.06kbits/s and 87.54kbits/s to 77.07kbits/s, 92.38kbits/s and
102kbits/s respectively across the three GSM cell clusters.
KEY WORDS
Quality of Service, Grade of Service, Network Optimisation, End user Satisfaction
INTRODUCTION
In recent time, mobile telephony has become one of the fastest growing and most demanding
telecommunications applications especially since the introduction of the second generation (2G)-
based Global System for Mobile communications (GSM) [1]. The GSM group was founded in
1982 and till today, it remains one of the leading mobile communication systems with over a
billion end users and a market share of 70% [2]. However, this has not also be without the
provision of good service quality changes. Therefore, to maintain the above growth and retain
existing customers, as well as attracting new ones, the GSM service providers need to deliver its’
services, consistently, reliably and economically , as the customer satisfaction on the service is
DOI : 10.5121/ijwmn.2014.6508 93

directly dependent on the quality and the performance of the network. More importantly, mobile
telephony service providers must be ready to continue to maintain the highest quality of service
(QoS) to both residential and industrial subscribers. Therefore, once a radio telephone network is
designed and operational, its performance should be monitored to improve overall service quality.
That is where network performance monitoring for QoS assessment, analysis of faults and
corrective actions comes in. In addition to monitoring network faults, the operator also needs
immediate information on how the network performs, especially from the end user perspective.
The end user network performance report can be used as source of information for corrective
actions or for evaluating the utilization of resources. This in turn enables the mobile telephone
operators to optimize traffic and investigate critical areas where the network performance could
require adequate changes. Also, network optimization engineers should make effort on regular
basis to improve the service quality and capacity of operational networks, as well as developing
and deploying new ones in order to meet customer demands. Moreover, carrying out regular
optimization remain essentially the only way to keep track of the network by looking deep into
network operation statistics and collecting/analyzing drive test data.
94
Problem Statement
In recent times, it has been observed that not all the telecom operators are giving due attention to
the quality of service (QoS) in Nigeria. Service quality of some mobile telephone networks
instead of being improved has been following a deteriorating trend (e.g. see our previous work in
[3]). With the increase of the subscriber base, customers’ complain has also increased. Today,
some of the common issues being faced by the subscribers are recurrent call drop, poor network
coverage and unsatisfactory customer care support. As a result, customer's dissatisfaction is
increasing and complains against the network are also increasing, which in turn leads to low
customers loyalty and high churn rate. In some cases, operators are implementing new features in
their networks without performing proper pre-trial which is also a reason for poor QoS.
Consequently, how to carry out a proper network optimization to resolve the above mentioned
problems and improve the end users satisfaction rate with the good quality and grade of service
(GoS) is the real challenge.
Research Methodology
Generally, network optimization can be seen as a very involving process as a large number of
variable are available for tuning impacting different aspect of the network performance. The
process involves the use of various methods to maximize the system performance by optimally
configuring the network and utilizing its resources. To simplify this process, a step by step
method is required. In this study, the following methods are adopted to achieve our goal:
1. Single site verification exercise
2. Pre Swap Drive Test
3. log-file analysis
4. Implementation of recommendation proffered for optimization
5. Post Swap Drive Test
6. Post Swap Drive Test Report and Analysis
In the first place, Single site verification exercise enables one to verify the status of the base
stations within the cluster to be optimized. Here, the individual sites are verified to make sure
they are free of critical hardware problems before the optimization process is kick off. Also, in
this exercise, some key engineering parameters like antenna height, antenna tilt, transmit power,

among others, are properly checked in each of the cell sites for errors and inconsistency with that
in the site data obtained before the optimization exercise.
This is followed by collecting and analyzing data from Pre Swap Drive Test carried out on the
selected routes and also data from networks nodes by using customized software.
After collection, the log-file is analyzed to identify possible problems within the networks. This is
done to improve the network performance.
Implementation of recommendation involves tuning the engineering antenna parameters such as
the antenna tilt, and parameter auditing.
This followed by carrying out another drive test ( i.e Post swap drive test) exercise to ascertain the
effect of the changes in the network parameters. The Post Swap drive test report will include the
same reports as the Pre Swap Drive Test.
After each optimization exercise, the Key Performance Indicators (KPIs) obtained are analyse and
checked against the desired threshold. See figure 1 for a summary of network optimization
process.
In this paper, we specifically engaged a combination of essential KPIS such as dropped call rate,
call setup success rate and outage call rate to examine overall QoS and GoS performance of the
investigated GSM network. This in turn allowed us to determine the end user satisfaction rate and
the general network performance. This exercise took place after some key problems such as poor
transmission line performance, reduced power output, cell coverage degradation, coverage hole,
over shooting, cell imbalance, poorly connected feeders, Increased interference and among others
were identified across the studied GSM cell clusters, the following recommendations were made
and implemented on the network to optimise its performances:
95
• Addition of missing adjacent cells in each cell cluster
• antenna azimuth and tilt changes
• BTS Equipment/Filter change
• Re–tuning of interfered frequencies
• Adjustment accessibility parameters
• power parameters changes
• Increase the reuse distance between the co-frequency and adjacent frequencies.

96
Figure 1: Network Optimization flowchart.
The Investigated Environments
This study was carried out with the need to enhanced end user satisfaction as desired by one of
the leading cellular network service providers in South East, Nigeria. The operator recently
engaged one of the equipment vendors in the country for BTS swapping across so some selected
cities in Asaba Area, South East Nigeria. To carry out the swap drive test experiment, the study
area was divided into three clusters. Each cluster has 26-40 sites on Air.
In order to find swapped feeders, a test was performed by driving round the cell sites to secure
that the planned physical cells’ identities are in order. The lists of the sites ID are given in table 1-
3.
Performance Measures
In telecommunication engineering, and in particular teletraffic engineering, the quality of voice
service is specified by two measures (Jang-Sub et al, 2008) [4]: the Grade of Service (GoS) and
the Quality of Service (QoS). In the ITU-T Recommendation E.800 [5], QoS is defined as the
collective effect of service performance, which determines the degree of satisfaction of a user of
the service. How the end-user perceives the service quality, i.e. his satisfaction with the service
as a function of the measured parameters, is highly dependent on the service level (QoS–
parameters) he is accustomed to. QoS is a measure of the Key Performance Indicators (KPIs).
GoS is a subset of QoS and it is the mechanism for controlling the performance, reliability and
usability of a telecommunications service. The smaller the value of GoS, the better is the service.
A telephone service is available if the GoS is better than a specified percentage. Availability is the
QoS characteristic that represents the proportion of time when satisfactory service is available.
QoS-Based Key Performance Indicators (KPIs)
According to ETSI’s standards [6] QoS KPIs can be categorized in five phases during service use
from the customer's point of view:
1. Network Availability: The probability that a service is available when the end user requires it,
is called availability.
2. Network Accessibility: Probability that the user performs a successful registration on the
network.
3. Service Accessibility: Accessibility has to do with the end users being able to set up a call and
access radio resources. If the user wants to use a service, the network operator should provide
him as fast as possible access to the service. Includes KIP’s that describe how successful is
the service access (e.g, call setup rate) as well as KPI’s that describe setup time ( i.e, call
setup time).
4. Service Integrity: This describes the QoS during service use. Includes KPI’s that describe the
quality of the service.
5. Service Retainability: Retainability covers the ability to keep up a call; either in accordance
with or against the will of the user. It Includes KPI’s that count the time that a service can
retain (Drop Call Rate or call holding time).
The service quality performance indicators we will consider in this paper cover the different
phases of QoS [6] aspects during service use from the customer's point of view. They are here
defined by:

• Call Setup success rate (CSSR): The measure of the service accessibility provided by
GSM network is the call set-up rate (CSSR). This indicator measures the ease in which
calls are established or set up. It analyses the ability to successfully establish voice
communications between two ends, a mobile network terminal and a fixed network
terminal, and the ability of networks to maintain this call during a pre-established period
of time. Mathematically, CSSR describes the ratio between the calls successfully
connected to the system and the overall number of call attempts:
97

(1)
where Nattempt is the number of call attempts and Nblock is the number of blocked calls.
• Drop Call rate (DCR): The measure of the service retainability, which describes the
termination of services in accordance with or against the will of the end user is the CDR.
It is defined by the ratio between the number of dropped calls and the overall number of
call releases:

(2)
where Ndrop is the number of dropped calls and Nrelease is the overall number of call
releases.
• Outage rate (OutR): This indicator measures service integrity, which is the QoS during
service use. OutR describes the ratio between the number of calls which, although
normally released, perceived an unacceptable outage time lasting more than 5% of the
duration of the call, and the overall number of call releases (that is both the normal and
the abnormal releases) [7]:

(3)
where Noutage is the number of calls with unacceptable outage time.
• End points Service Availability (ESA): This indicator measures service availability,
which is the QoS during service use and it is appraise from the view point of what the
customer get from their end. Endpoints are defined as the interface between the customer
and the equipment providing access to the service. ESA is defined as the percentage of
time a usable call can be established and maintained between two end points. The
measurement of ESA is described by the ratio:
!

(4)
• Satisfaction rate (SatR): it summarises the degree of voice user satisfaction, by
considering altogether service accessibility, service retainability and service integrity. A
call is considered satisfied if it isn't blocked, nor dropped, nor it didn't feel outage [7]:
SatR = [CSSR (1 - (DCR + OutR)] (5)

X
#

(7)
where Nsat is the number of satisfied users, that is the calls which were successfully connected to
the network and were normally released without perceiving significant outage during the call.

International Journal of Wireless Mobile Networks (IJWMN) Vol. 6, No. 5, October 2014
98
GoS-Based Key performance Indicators (KPIs)
In GSM networks handle both voice and data traffic requirements of the mobile communication
by providing two modes of operation:
• Circuit switched (which caters for voice based traffic) or
• Packet switched (which caters for data traffic)
Circuit switching provides the customer with a dedicated channel all the way to the destination.
For circuit switch services in GSM, blocking probability can be successively used as a
performance indicator for the evaluation of GoS. It expressed by the formula:
'$()'*+,-./0'(1(,),2%

#

(8)
The above expression provides a measure of the ability of a user to access the system during the
busiest hour which is specified as the probability of a call being blocked.
Here, the GoS for packet switch services is expressed in terms of throughput which represents the
long-run throughput per service. The throughput is derived by considering the long-run rate at
which the user enters the air interface and the data rate per user by employing the following
formula [8]:
340'.4/ 5$6 7 ()'*+,-./0'(1(,),2% (9)
where
R, is the data rate of service blocking probability and the call arrival rate, 8 refers to the traffic
offered expressed as the number of call attempts per unit time which is given by [9], [10]:
5

9:;;
(10)
With packet switching, the operator assigns one or more dedicated channels specifically for
shared use. These channels are up and running 24 hours a day, and when you need to transfer
data, you access a channel and transmit your data. The standard data rate of a GSM channel is
22.8 kbps.
RESULTS AND DISCUSSION
Tables 1-3 and figures 2-4 shows the network performances at studied three cell cluster
environments using the basic KPIs. It can be clearly seen from the tables that performance of the
network before and after optimisation showed significant performance improvement in terms of
call drop rate, call set up success rate, and call block rate.
Figures 6a and 6b show the radar and the bar plots of end user satisfaction rate performance at
various GSM cell clusters. The distribution of end user satisfaction rate before optimisation shows
an increased in performance from 94.45%, 87.74%, and 92.85% to 99.05%, 95.38% and 99.03%
respectively after optimisation across the three GSM cell clusters. In addition, the GoS as shown
in the plots of figure 8 is reduced from 3.33%, 6.60% and 2.38% to 0.00%, 3.70% and 0.00%
before and after optimisation respectively. Furthermore, in figure 7, ESA performance, which
correspond end points service availability, has improved from 94.44%, 93.40% and 97.62% to
100%, 96.30% and 100% before and after optimisation respectively. Also, the average throughput
has improved from 73.74kbits/s, 85.06kbits/s and 87.54kbits/s to 77.07kbits/s, 92.38kbits/s and

International Journal of Wireless Mobile Networks (IJWMN) Vol. 6, No. 5, October 2014
102kbits/s respectively across the three GSM cell clusters as showed in figures 9-11 before and
after optimisation across the investigated GSM cell clusters.
99
Cluster ID: CLUSTER 1
Indicators Swap Before Swap After
% Blocked calls 3.33 0.00
% Dropped calls 2.30 0.95
% Established calls 96.67 100
% Good calls 97.70 99.50
% Call Set-up Success 96.67 100.0
Outage calls 0.00 0.00
Average
73.74 77.06
Throughput(kbits/s)
Table 1: 1st Cell Cluster, Unoruka Road, Akwa Etiti and environs.
Table 2: 2nd Cell Cluster, Awada Onitsha and environs
% Established calls 93.40 96.30
% Good calls 93.94 99.04
% Call Set-up Success 93.40 96.30
Average
85.06 92.38
Throughput(kbits/s)
Table 3: 3rd Cell Cluster, Palobis Place Onitsha and environs
% Established calls 97.62 100
% Good calls 95.12 99.02
% Call Set-up Success 97.62 100
Average
87.54 102.65
Throughput(kbits/s)

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