Vol. 6, No. 3 March 2015
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2015 CIS Journal. All rights reserved.
http://www.cisjournal.org
A Study on the Hourly Behavior of Key Performance Indicators of
Global System for Mobile Communications
1
Maria Andleeb, 2 Syed Abbas Ali
1
Lecturer, Department of Computer Engineering, UIT, Karachi
Astt.Prof, Department of Computer & Information Systems Engineering, NED University, Karachi
1
maria_13augi@hotmail.com, 2 saaj.research@gmail.com
2
ABSTRACT
In order to judge the quality of service (QoS) and the network performance, GSM operators use Key Performance
Indicators (KPI) regarding end user perspective. KPI are derived with the help of counters which are triggered in the Base
Station Controller (BSC) whenever there is an event occurrence over air interface. This paper presents the network
performance of real time GSM radio frequency (RF) on the basis of KPI calculations. In this paper the hourly behavior of
six major KPIs (SDR, TCH, SDCCH, DCR, HOSR, ULI) are examined by simulating separate functions of each KPI for 6
days (24*6= 144 hours). The experimental analysis shows that the Uplink (UL) interference KPI and Handover Success
Rate (HOSR) KPI are perform better as compared to the other KPIs.
Keywords: Base Station Controller (BSC), Radio Frequency, GSM Network, Key performance indicator (KPI), counters.
1. INTRODUCTION
A large number of factors in the environment
affect the signal that is transmitted by the transmitting
antenna (from Base Transceiver System to Mobile
Station) and is received by the receiving antenna (by
Mobile Station from Base Transceiver System). The
coverage, capacity and quality are being the three factors
on which the success of GSM network depends. Quality
can be improved by eliminating the factors that affects the
KPIs performance, both from external and internal
sources [1]. The quality of service is totality of the
characteristics of the telecommunication services that bear
on its ability to fulfill the needs and satisfy the end users.
Network performance is evaluated towards the quality of
service (QoS) as intended by the end users [2]. The
relationship between the event counters, KPIs and QoS
report is shown in Fig.1. [3]. Likewise to evaluate the
network performance, KPI’s and drive test are used in [4].
It was concluded from the comparative study that
TCH drop rate is reduced from 0.76% to 0.62% whereas
the handover success rate is increased from 95.75% to
96.13%. Similarly a comparative study of the GSM sites
of mobile networks is presented [5]. The features and
parameters on the basis of performance of GSM network
can be as follows: Blocked Call Analysis, Drop Call
Analysis, Speech Quality Parameter, and Speech quality
Analysis. In a study, the Quality of Service (QoS) of the
Nigerian GSM operators using a real time Methodology
(RTM) is carried out. A stated hypothesis was accepted in
this study following the drops in the KPI ratings of
operators [6].
Fig 1: The relationship between event counters, KPI’s and
Quality of service [11].
The QoS evaluation in GSM using KPIs is
identified in [7]. Network accessibility, service retains
ability, connection quality and network coverage was
considered as the four assessment parameters in this
study. Optimization of GSM network, data sorting and
analysis was addressed in [8] which listed the top ten
wireless parameters of GSM network. A pilot study was
developed on the performance of the GSM network [9],
which was aimed at optimizing network performance.
Various parameters were accessed and recommendations
were made by carrying out the simulations to improve the
efficiency of wireless communication networks. Kuboye
identified the congested areas on the GSM network,
which analyzed the QoS and causes of congestion on
GSM network [10]. The focus of this paper is to analyze
the hourly behavior of six KPIs (SDR, TCH, SDCCH,
DCR, HOSR, ULI) for GSM network based on the values
of counter sheet obtained from GSM service provider
.The rest of the paper is organized as follows. Research
methodology is discussed in Section 2. Experimental
results are presented in section 3. Finally, section 4
concludes the experimental results.
2. METHODOLOGY
The presented experimental framework for
analyzing the hourly behavior of key performance
indicators (KPIs) of GSM network is divided into three
different stages:
169
Vol. 6, No. 3 March 2015
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2015 CIS Journal. All rights reserved.
http://www.cisjournal.org
1.
2.
3.
KPI Monitoring
Merging of Excel Sheets
Calculating KPI Values
2.1 KPI Monitoring
The purpose of monitoring the values of certain
KPIs associated with the GSM air interface such as SDR,
TCH, SDCCH, DCR, HOSR, and ULI is to monitor the
trends associated with these KPIs and that take certain key
optimization decisions in order to rectify any problem in
the network[12]. The values of counters were taken from
the BTS sheet generated by the GSM service provider, in
which count of any event generated in counter. We made
use of these values of KPIs measure to calculate KPIs
using their respective formulas. Monitoring is done by
plotting values of KPIs against time and certain thresholds
were defined for each KPI which can help to take
optimization decisions.
Each KPI function is referencing the merged excel sheet
for calculating the corresponding values.
2.3.1 Session Defect Ratio (SDR):
In relation to the total number of call attempts,
SDR is the percentage of call attempts receiving the
response as server internal error or server timeout. It is the
ratio of number of the call attempts which are associated
with server error and total number of call attempt request.
SDR= ((MC07+MC137+MC138)*100)/(MC01+MC02))
(1)
Where MC07 counts the call attempts associated
with server error, MC137 and MC138 counts the call drop
on Standalone dedicated control channel (SDCCH) and
MC01 and MC02 counts the successful SDCCH seizures.
2.3.2 Drop Call Rate (DCR):
This KPI monitors the fraction of the telephone
call drop due to technical reasons. It is measured as the
percentage of all calls. The ratio of number of call drops
and number of calls initiated including number of
incoming handover.
DCR=
((MC621+MC736+MC14C+MC739+MC921C)*100)/
(MC718+MC717A+MC717B-MC712)
(2)
Fig 2: Merged Excel Sheet of BSC
2.2 Merging of Excel Sheets
The counter sheets of BTS, containing values of
counters measured after every hour regularly for 6 days
for each BTS in one BSC network having total of
24*6=144 excel sheets. MATLAB toolkit was used for
calculating and plotting KPI values for continuous 6 days
period. In order to increase the efficiency associated with
these large numbers of excel sheets files, we merged all
these 144 sheets containing counters value into a single
sheet using MATLAB toolkit. Each of 144 sheets contains
counters value of every BTS connected to common BSC;
this means that the merged sheet can be associated with a
particular BSC. After gone through this process instead of
referencing multiple sheets only a single sheet will refer
for calculating KPIs with increasing
processing
efficiency and time consumed in searching multiple
sheets. Counter sheet is illustrated in the Fig. 2.
2.3 Calculating KPI Values
In order to calculate KPI values, functions have
been created for each KPI. The advantage of creating
functions is that value of each KPI can be explicitly
calculated in any program. Calculation of 6 most
important KPIs (SDR, TCH, SDCCH, DCR, HOSR, ULI)
have been done which is used in air interface monitoring.
Where MC621, MC736, MC14C, MC739 and
MC921C are the counters to count the total number of call
drops that occurred during the conversation phase,
MC718, MC717A and MC717B count the number of calls
initiated on the cell and MC712 counts the number of
incoming handover.
2.3.3 Hand over Success Rate (HOSR):
Handover success rate directly affects the user
performance and is an important KPI of hold call type.
When the user traverses different cells, this KPI enables
user to communicate continuously. It is the ratio of
successful handover and total handover requests.
HOSR= (100*(MC656+MC646))/ (MC660+MC650))
(3)
Where MC656 and MC646 count the successful
handover and MC660 and MC650 count the total
handover requests.
2.3.4 Standalone Dedicated Control Channel Success
Rate (SDCCHSR):
SDCCH success rate is the percentage of Mobile
Station (MS) call setup success due to Time slot
availability at the first call. It is the ratio of the call setup
success including the call setup failure and the total
number of calls setup.
SDCCHSR= (100*(MC01+MC02))/((MC04+MC148)))
(4)
170
Vol. 6, No. 3 March 2015
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2015 CIS Journal. All rights reserved.
http://www.cisjournal.org
Where MC01counts the total number of call setup
success, MC02 counts the total number of call setup
failure and MC04 and MC148 count the total number of
calls setup.
2.3.5 Traffic Channel (TCH) Traffic Carried:
For the circuit switched traffic, this KPI provides
the average number of time slots that are in use
simultaneously.
TCH traffic carried= (MC380B+MC380A)/3600
(5)
Where MC380B and MC380A are the counters
that count the mean holding time of TCH channels.
The graph of SDR analysis in Fig.3 shows the
results that mean highest session defect occurs during the
7th hour due to the large number of server errors while
and the lowest session defect occurs during the 13th hour
of the day in all 6 days. The high session defect rate can
be improved by controlling the SDCCH congestion. The
suggested threshold level for SDR is 0.8 rate/hr.
Fig. 4 shows the Drop Call Rate (DCR) Analysis.
The graph of DCR shows that the highest drop call rate
occurs during the 5th hour of the day and the lowest drop
call rate occurs during 3rd and 4th hours of the day. The
performance can be improved by maintain the radio
coverage and managing the load on the cells. The
suggested threshold level for DCR is 1.8 rate/hr.
2.3.6 Uplink (UL) Interference:
This KPI is used to estimate the uplink capacity
that is limited by the UL interference of all cells in one
Radio Network Controller (RNC). It is the ratio of the
number of cells with wide band power of 98dbm and total
number of cells in RNC.
UL
interface=
(100*MC676)/
((MC670+MC671+MC672+MC673+MC674+MC676+
MC677+
MC678+MC679+MC785A+MC785D+
MC785E+MC785F+MC586A+MC586B+MC586C+MC1
040
+ MC1044+MC449)+ 0.000001)
(6)
Where MC676 counts the number of cells with
wide band power of 98dbm and MC670, MC671, MC672,
MC673, MC674, MC676, MC677, MC678, MC679,
MC785A, MC785D, MC785E, MC785F, MC586A,
MC586B, MC586C, MC1040, MC1044 and MC449
count the total number of cells in RNC.
3. EXPERIMENTAL RESULTS
Fig 4: Drop Call Rate (DCR) Analysis.
The graph of HOSR is presented in Fig. 5 which
shows that the highest handover occurs during the 3rd and
6th hour while the lowest handover occurs during the fifth
hour. The problem of high handover rate can be improved
by maintaining the uplink and downlink congestion and
hardware and transmission failures. The suggested
threshold level for HOSR is 98 rate/hr.
We made use of MATLAB toolkit for producing
and monitoring their values by plotting the KPI values
against time. KPI monitoring could be done for a day (24
hours) or the plot can be made for a whole week. Here a
particular threshold level could also be defined for each
KPI, specifying any significant drop in our service. We
plot the hours by considering the merged excel sheet
containing 24 hour’s count of 6 days.
Fig 5: Handover Success Rate (HOSR) Analysis
Fig 3: Session Defect Ratio (SDR) Analysis
171
Vol. 6, No. 3 March 2015
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2015 CIS Journal. All rights reserved.
http://www.cisjournal.org
The graph in Fig.8 shows that the interference is
high during the 2nd hour of the day while it is zero during
most of the hour. The performance of UL interference
KPI is good enough and it can be further improved by
maintaining a threshold for power level transmission and
setting the proper filter adjustment in the spectrum
analyzer. The suggested threshold level for UL
interference is 0.1 rate/hr.
4. CONCLUSION
Fig 6: Standalone Dedicated Control Channel Success
Rate (SDCCHSR) Analysis
In Fig.6 the graph shows that SDCCH Success
Rate has the highest rate during the last 24th hour of the
day while the lowest rate during the 7th hour of the day.
The poor functioning of this KPI should be improved by
maintaining the high timing advance and avoiding the
unknown access cause code. The suggested threshold
level for HOSR is 95.5 rate/hr.
Fig.7 shows that TCH has high traffic carried
during the 20th hour and the low traffic carried during the
4th hour in all 6 days of the week. The reasons that should
be improved in this regard can be the high antenna
position and low handover activity. The suggested
threshold level for TCH traffic carried is 14 rate/hr.
This paper analyzed the hourly behavior of six
Key performance indicator (KPIs)(SDR, TCH, SDCCH,
DCR, HOSR, ULI) in order to examine the quality of
service (QoS) in GSM network based on the event occur
in an air interface. The KPIs calculation based on the real
time data by simulating discrete functions of each KPI for
6 days (24*6= 144 hours). Experimental analyses have
been simulated on MATLAB tool kit using BTS
generated
counter
values.
Experimental
study
demonstrated that Hand over success rate (HOSR) and
Uplink Interference shows significant performance in
comparison with other KPIs. In future research work,
focus will be on some others KPI’s (Handover Failure
Rate, Traffic Handoffs, Location updates, Uplink Level)
for evaluating the network performance of GSM.
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Fig 7: Traffic Channel (TCH) Traffic Carried:
Fig 8: Uplink (UL) Interference Analysis
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Vol. 6, No. 3 March 2015
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences
©2009-2015 CIS Journal. All rights reserved.
http://www.cisjournal.org
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AUTHOR PROFILES
Maria Andleeb, received M.Engg. Degree from NED
University of Engineering &Technology, Karachi. Her
areas of interest are Digital Signal Processing and
Automatic Speech Recognition.
Syed Abbas Ali, received M.Engg. Degree from NED
University of Engineering &Technology, Karachi. His
areas of interest are Machine Learning and Automatic
Speech Recognition.
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