=Paper=
{{Paper
|id=Vol-1830/Paper91
|storemode=property
|title=Traffic Congestion Analysis in Mobile Macrocells
|pdfUrl=https://ceur-ws.org/Vol-1830/Paper91.pdf
|volume=Vol-1830
|authors=Aliyu Ozovehe,Okpo U. Okereke,Anene E. C.,Abraham U. Usman
}}
==Traffic Congestion Analysis in Mobile Macrocells==
https://ceur-ws.org/Vol-1830/Paper91.pdf
International Conference on Information and Communication Technology and Its Applications
(ICTA 2016)
Federal University of Technology, Minna, Nigeria
November 28 – 30, 2016
Traffic Congestion Analysis in Mobile Macrocells
Aliyu Ozovehe1, Okpo U. Okereke2, Anene E. C.2, and Abraham U. Usman3
1
Trifield Technology Limited, Abuja, Nigeria
2
Department of Electrical and Electronics Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria
3
Department of Electrical and Electronics Engineering, Federal University of Technology, Minna, Nigeria
1
aliyu123oz@gmail.com
Abstract—Traffic congestion during busy hour (BH) pattern of busy hour traffic is considered for congestion
deteriorates the overall performance of cellular network and evaluation [5] using key performance indicators (KPIs).
may become unmanageable unless effective and efficient These KPIs are defined for different interfaces and
methods of congestion control are developed through real live network elements of GSM/GPRS. To cater for subscriber
traffic data measurement and analysis. In this work, real live demand, radio frequency (RF) optimization teams use the
traffic data from integrated GSM/GPRS network is used for KPIs to generate quality of service (QoS) reports to ensure
traffic congestion analysis. The analysis was carried out on 10 minimal congestion over all the interfaces and network
congesting cells using network management system (NMS) elements in order to avoid QoS degradation by maintaining
statistics data that span three years period. Correlation test
the KPIs under pre-defined threshold [6].
was used to show that TCH congestion depend only on call
setup success rate (CSSR) and BH traffic at cell level. An
Network congestion leads to poor QoS which affect
average correlation coefficient value of 0.9 was observed grade of service (GoS) of the network, particularly during the
between TCH congestion and CSSR while 0.6 was observed busy hour of the day [7]. In a loss system, the GoS describes
between TCH congestion and BH traffic. The correlation test is that proportion of calls that are lost due to congestion in the
important when selecting input for congestion prediction busy hour and can be measured using equation (1):
modeling.
Number _ of _ lost _ calls
Keywords-traffic congestion; GSM; GPRS; macrocells GoS (1)
Number _ of _ offered _ calls
I. INTRODUCTION
While a whole range of services of GSM technology are
All over the world, cellular network operators are faced in use in Nigeria, it is obvious that the network performance
with the challenges of improving the quality of service (QoS) in terms of QoS are degrading which proved that GSM
while increasing capacity and rolling out new services. This network is either over utilized or under dimensioned. Hence
has resulted in many congested networks and consequently the need for this analysis to identify the cells that are
degradation of QoS due to inadequate provision of the responsible for congestion during busy hour by statistically
needed resources or underutilisation of the available analyzing traffic data of a live network in order to establish
resources. the presence or absence of congestion.
To cope with subscriber demands and meet Regulator
standards, cellular network providers dimension network
elements on continuous based using network management II. LITERATURE REVIEW
system (NMS) statistics, drive test trailing and customer
feedbacks. Traffic analysis is important for evaluating the
However, Nigerian Communication Commission (NCC) performance of existing networks and network optimization.
quarterly audit reports of GSM network performance had The events that occur in BTS trigger different counters in the
consistently shown that the operators have not been able to BSC and MSC memory that are used for performance
meet the set standards [1] due to network congestion [2]. If monitoring and network evaluation. Various KPIs that are
there is no hardware fault, network congestion usually occurs used to measure network performances are derived with the
due to insufficient number of radio channels in access help of these counters using different formulations [7]. In
network elements [3]. practice, the performance can be monitored at different
This work used busy hour traffic data of access network sections of the network [8] and in this work the network
from a live network to analyse traffic congestion in some performance is evaluated at cell levels in terms of resource
macrocells of GSM/GPRS network. The busy hour of a allocation and resource utilization.
network is the time when the network processes the Some of the early works on GSM network elements
highest traffic in a day and it is used to measure network performance were done mostly on access part of the network
performance, determine the robustness of a network and its particularly at BTS level. For example, [9] proposed a traffic
dimension [4]. To measure the network performance, the model for mobile network, using Markov chain to determine
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
call blocking and handoff failure probabilities when no standalone computer and exported to Microsoft Excel
queuing of new or handover calls is performed while [10] environment.
modeled the effect of user mobility on the performance of The network is composed of 742 cells, 262 BTSs.
mobile networks using office hours traffic data. Location Measurements were taken from November, 2012 to
updates were analyzed to evaluate the probability of call September, 2014. Correlation test was used to choose only
dropping when handover is needed. Likewise, [11] analyzed KPIs that have significant effect on TCH congestion during
seventy eight traffic channels and showed that a single busy hour.
dedicated channel is enough for good QoS. [12] investigated Correlation coefficient is defined as a number or function
GSM/GPRS system performance using dedicated number of indicating the degree of correlation between two variables
GPRS channels and some idle periods between voice calls like X and Y. In this work, the variables are busy hour traffic
for GPRS data packet transfers. Reservation of more and CSSR, HOSR, DCR, SDCCH and TCH Congestions as
channels brings handover failure and dropped call KPIs to measure the network performance. Equation (2)
probability to very small values but lack of ordinary channels defines correlation coefficient as:
produces a larger new calls blocking probability.
The work of [13] presented the results of study of
DCS1800 Um-interface using daily traffic measurement Correl ( X , Y )
x x y y (2)
data. The performance indicators used are Traffic, CSSR,
x x y y
2 2
handover success rate (HOSR), standalone dedicated control
channel (SDCCH) and traffic channel (TCH) congestion.
The analysis shows the limitations of the system to Microsoft excel statistical tools was used for the
accommodate extreme offered traffic without adding more correlation analysis.
resources. A model combining simulations for paging,
signaling and traffic channels was developed by [14] to
investigate the optimal dimensioning of a finite physical IV. BTS DAILY BUSY HOUR TRAFFIC ANALYSIS
resource allocated across multiple logical channels with
multiple traffic types. The busy hour traffic data showed that 154 cells
Reference [15] evaluated the performance of GSM1900 experienced congestion out of 742 cells during the period
Um-interface of two different vendors using daily under investigation. In the cell KPIs analysis, 10 worst
measurement data for one week. The performance indicators congesting cells were chosen for BH TCH congestion
used are peak hour traffic; CSSR; Handover Failure; analysis [19].The ten most congested cells is shown in Table
congestion on control channels (SDCCH blocking rate); 1, Table 2 presents the maximum and average daily BH
congestion on traffic channels (TCH blocking Rate); drop on traffic of the ten most congested cells over the period.
traffic channels; drop on control channels; cells with TCH
congestion rate exceeding 2% and TCH Mean Holding
Time. V. BTS TCH CONGESTION CORRELATION TEST ANALYSIS
In another work, [3] analyzed traffic data from a trunked Using the BTS KPIs and BH traffic, the correlation test
radio network operated by Ecomm in UK using OPNET showed that the ten worst cells behaved differently in terms
model. The findings indicated that traditional Erlang models of KPIs that have strong correlation with TCH congestion
for voice traffic may not be suitable for evaluating the during busy hour which implied that they must be
performance of trunked radio networks. In a related work, investigated differently based on their correlation results.
[16] formulated a dynamic channel allocation model using However, we selected 4 cells to know which KPI has strong
Markov chain technique as an improvement on [17]. There is correlation with TCH congestion at cell level. The result of
one problem common to all these works at BTS level, the test for the four cells is summarised in Table 3.
exclusive handover channels were employed for easy Table 3 shows that BH TCH congestion has strong
compliance of QoS standards for ongoing calls and handover correlation with CSSR and busy hour traffic for the four
failure reduction. However, the disadvantage is that new cells.
calls blocking increase as a result of the reduction of Given the result of the correlation test, the maximum
available ordinary channels. The solution should have been daily BH traffic carried by the ten cells and it impact on
that resources should be added to maintain GoS of the CSSR and TCH congestion is shown in Figure 2 and Figure
network as put forward by [18] and supported by the work of 3.
[13]. From Figure 2 and 3, most of the cells CSSR and TCH
CONG degraded when they carried maximum traffic while
III. METHODOLOGY Cell 396B, 393C and 301C suffered worst KPIs degradation.
The setup for data collection in GSM/GPRS network The behaviour of the ten worst cells when they
comprises of base station subsystem (BSS) and network experienced worst TCH CONG and the effect on other KPIs
subsystem (NSS) connected to standalone system called during the period is shown in Figure 4 to 6.
NMS as shown in Figure 1. All the cells CSSR degraded and their ability to carry
NMS is the functional entity from which the service traffic is limited when they experienced worst TCH CONG
provider monitors and controls the entire network. The data while Cell 496B, 430C, 396B and 038B recorded worst KPIs
used in this work was extracted from the NMS with the help degradation. This shows that traffic channel of these cells are
of Ericsson Business intelligent (BI) tools installed on the not properly dimensioned.
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
VI. CONCLUSION BH traffic has an average correlation coefficient of 0.9 and
The analysis of traffic channels in an network existing 0.6 respectively for the cells. The strong correlation showed
showed that TCH congestion beyond the acceptable 2% that the knowledge of CSSR and busy traffic can be used to
threshold for traffic channel (TCH) occurred in 154 cells out predict TCH congestion which is crucial for cellular network
of 742 cells investigated. optimization and resource management.
The busy hour TCH congestion analysis showed that the
congestion depends on CSSR and BH traffic. The CSSR and
Figure 1. System for collecting traffic data
TABLE I. TEN WORST CONGESTING CELLS
Cell Ids
Dates
FC0396B FC0301C FC0362C FC0385B FC0393C FC0430C FC0494B FC0496B FC0038B FC0725C
Jan-13 3.98 60.18 80.01 2.14 4.35 78.95 46.04 69.59 3.7 -
Feb-13 14.44 46.81 23.86 27.91 13.83 24.64 19.92 - 14.67 -
Mar-13 50.87 3.96 62.71 - - - - 20.4 2.41 44.16
Apr-13 20.78 30.47 50.41 32.12 19.74 24.28 55.9 - 7.53 -
Jun-13 15.31 48.24 59.02 9.39 21.63 28.87 33.77 10.23 2.39 -
Jul-13 10.02 53.61 69.77 4.11 14.72 13.31 17.12 7.65 - 30.85
Aug-13 7.15 37.83 67.51 2.17 18.28 18.13 16.78 3.19 - 38.36
Sep-13 10.18 33.95 65.27 - 19.57 5.26 14.01 1.86 - 16.22
Oct-13 9.99 52.04 61.52 3.1 21.07 12.43 21.69 6.05 16.97 2.76
Nov-13 13.8 58.28 63.61 3.03 11.34 3.68 36.6 4.32 - -
Dec-13 3.42 74.94 84.54 13.31 18.45 9.28 31.63 32.65 - -
Feb-14 16.65 71.21 80.59 14.57 9.69 82.61 54.85 83.13 17.22 19.15
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
TABLE II. DAILY BH TRAFFIC (ERLANG) FOR THE TEN WORST TABLE III. SUMMARY OF TCH CONGESTION CORRELATION TEST
CELLS
TCH Correlation
Cell Max Traffic Average Traffic
C H SDC
FC0725C 25.6 15.01 S D O CH
S C S CON Tra
FC0496B 43.93 18.11 Cell R R R G ffic
0. 0. 0.
FC0494B 21.68 13.61 FC0725C 9 2 1 0.4 0.5
1. 0. 0.
FC0430C 64.03 37.9
FC0393C 0 1 4 0.1 0.6
FC0396B 21.59 12.75 0. 0. 0.
FC0362C 9 7 1 0.0 0.7
FC0393C 19.82 13.85 0. 0. 0.
FC0301C 7 6 1 0.4 0.6
FC0385B 47.65 13.08 0. 0. 0.
FC0362C 27.66 15.77 Average 9 4 2 0.2 0.6
FC0301C 34.99 17.12
FC0038B 54.41 21.72
100.00 98 98
90.00
80.00
70.00
CELL KPI [%]
60.00
50.00
40.00
30.00
20.00
10.00
0.00
CSSR HOSR
FC0725C FC0496B FC0494B FC0430C FC0396B FC0393C
FC0385B FC0362C FC0301C FC0038B NCC
Figure 2. CSSR and HOSR for ten worst Cells during Max. Traffic
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
50.00
40.00
CELL KPI
30.00
20.00
[%]
10.00
2 2
0.20
0.00
DCR SDCCH CONG TCH CONG
FC0725C FC0496B FC0494B FC0430C FC0396B FC0393C
FC0385B FC0362C FC0301C FC0038B NCC
Figure 3. DCR, SDCCH CONG and TCH CONG for ten worst Cells during Max. Traffic
120.00
98 98
100.00
80.00
CELL KPI [%]
60.00
40.00
20.00
0.00
CSSR HOSR
FC0725C FC0496B FC0494B FC0430C FC0396B FC0393C
FC0385B FC0362C FC0301C FC0038B NCC
Figure 4. CSSR and HOSR for the Cells during Worst TCH CONG
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
35.00
30.00
CELL KPI [%]
25.00
20.00
15.00
10.00
5.00 2
0.20
0.00
DCR SDCCH CONG
FC0725C FC0496B FC0494B FC0430C FC0396B FC0393C
FC0385B FC0362C FC0301C FC0038B NCC
Figure 5. DCR and SDCCH CONG for the Cells during Worst TCH
40.00
35.00
30.00
CELL TRAFFIC [ERLANG]
25.00
20.00
15.00
10.00
5.00
0.00
TRAFFICC_WORST TCH CONG TRAFFIC_AVERAGE
Figure 6. Traffic Average and Traffic Carried by the Cell worst TCH CONG
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
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