Analysis of Mobile Traffic based on Fixed Line Tele-Traffic Models

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 7, 2011

Analysis of Mobile Traffic based on Fixed Line
Tele-Traffic Models
Abhishek Gupta Bhavana Jharia Gopal Chandra Manna
ME Student, Communication System Associate Professor, Department of EC Sr. General Manager
Engineering Branch Jabalpur Engineering College BSNL, Jabalpur
Jabalpur Engineering College, M.P., India M.P, India M.P, India
abhishek2800@gmail.com dr.bhavana.jharia@jec-jabalpur.org gcmanna@gmail.com

Abstract— An optimal radio network which provides users. However, In previous models the random variation of the
and handle the largest amount of traffic for a given real traffic behaviors are unknown or simply not taken into
number of channels at a specified level of quality of account in the modeling process, such models fall short of a
service are designed by accurate traffic clear Connection with the actual physical processes involves
characterization and a precise analysis of mobile that are responsible for the behavior observed in the traffic
user’s behavior in terms of mobility and cellular data.
traffic. This paper focuses on the traffic Characterization of GSM
network where differences between traditional model and
This paper reviews the statistical characteristics of practical data may occur. The selected GSM networks provided
voice and message traffic. It investigated possible a good conversational service to a population of mobile users in
time-correlation of call arrivals in sets of GSM both dense urban area like Calcutta and the other at rural area at
telephone traffic data and observes proximity of North Eastern province of India. A few sets of GSM traffic data
practical mobile traffic characteristics vis-à-vis has been collected during January 2011 from both areas and
classical fixed-line call arrival pattern, holding time were subjected to analysis in present research work.
distribution and inter-arrival pattern. The results The outline of this paper is organized as follows: section II
indicated dominance of applicability of basic traffic Describes the overview of the previous or classical models for
model with deviations. A more realistic cause for call describing traffic characterization in mobile networks. Section
blocking experienced by users has also been III introduces analytical approach of real traffic data to outline
analyzed. the statistical method of distribution for arrival processes and
Keywords: GSM, Poisson distribution, Exponential the channel holding time. Section IV traffic analysis result are
presented. Section V Concludes the paper.
distribution, Arrival pattern, Holding time Inter-arrival
Pattern.
II.BASIC TRAFFIC MODELLS AND PREVIOUS WORK
I. INTRODUCTION The traditional telephone traffic theory, developed for wired
GSM cellular network have undergone rapid developments Networks, call arrivals to a local exchange are usually
in the past few years. The operators are facing challenges to modeled as a Poisson’s process. The process assumes 1)
maintain an adequate level of quality of service with growing stationary arrival rate since the user population served by the
number of end users and increasing demand for variety of exchange is very large and 2) has negligible correlation among
services [1, 2]. users. These pair of assumptions is also applicable in cellular
networks for incoming calls. These assumptions leads to
The mobile communication system has a limited capacity; it
random traffic model shaped as Poisson process for analytic
can only support a limited amount of simultaneous traffic
simplicity.
especially in peak hours with appropriate Grade of Service
(GoS). In the past few Decades, several traffic models like According to Poisson distribution, the probability of n no of
Exponential model, Poisson models etc. for Cellular systems calls arrival in given time interval 0 to t is
have been proposed for predicting the behavior of mobile
traffic [3]. The mobile traffic models are derived by fitting the
existing traffic data obtained from experience of land-line
traffic.
Where, λ is the arrival rate.
A scale-free user network model was used by researchers
in the analysis of cellular network traffic, which Shown the In research at [5], it has been shown that Poisson’s
clear connection between the user network behavior and the assumption might not be valid in wireless cellular networks for
system traffic load [4]. The traffic performance of a Cellular a Number of reasons like when we concentrating on small area;
system is strongly correlated with the behavior of its mobile where possible correlation may occurs between users;

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presence of congestion; and the effect of handover occurs not specified, we consider all outgoing calls as call arrival in
frequently etc. mobile network for analysis purpose.

The second important parameter for mobile cellular network All Outgoing calls are initiated randomly; if a call arrives
planning is the channel holding time. It can be defined as the and the communication is successfully established, both the
time during which a new call occupies a channel in the given caller and the receiver will be engaged for certain duration.
cell, and it is dependent on the mobility of the user. In the past, The duration of the holding time is also a random variable.
it has been widely assumed as the negative exponential Thus, the traffic load depends on the rate of call arrivals and
distribution to describe the channel holding time [6]. the holding time for each call. Generally, Traffic
characteristics of mobile network are typically measured in
The probability of holding a call by a further time dt after terms of the average activity during the busiest hour or peak
holding the call up to time t is hour of a day [15].

This paper presents a design approach to characterize the
mobility related traffic parameters in the presence of real
The hypothesis of negative exponentially distributed
traffic conditions in urban area and rural area base on Cell
channel holding time is valid under certain circumstances [7].
The channel holding time has been also been showed to fit coverage. This includes the distribution of the arrival
lognormal distributions better than the exponential one [8]. processes and the channel holding time.
Also, several other works are also contradicted this simple We analyzed sets of GSM telephone traffic data, collected
assumption. In [9,10] the probability distribution that better for billing and traffic monitoring purpose which include call
fits empirical data, by the Kolmogorov-Smirnov test, was arrival time i.e. (Termination point of call) and the duration of
found to be a sum of lognormal distributions. calls at particular cell site. In addition, we also consider traffic
In some other works, it is shown that the channel other then voice calls like SMS service which may also affect
holding time is also affected by user mobility. It is the network performance. Un-answered calls attempt could not
characterized by the cell residence time i.e. period of stay of a be recorded and also no information was recorded to trace the
call in a cell. The cell residence time also follows definite user mobility between the cells, neither was they felt necessary,
distribution pattern. The channel holding time distribution was as totality of the calls were recorded and attributed to the
derived analytically [11, 12, 13] when the cell residence time originating cell.
has Erlang or Hyper-Erlang distribution. A further empirical All unsuccessful repeated call attempts, the impact of
study on GSM telephone traffic data reported in [14] where handovers and congestion were not taken into consideration
answered call holding time and inter-arrival times were found for present analysis. The different graphs have been plotted to
to be best modeled by the lognormal-3 function, rather than by find the relation between the actual data and the classical
the Poisson and negative exponential distribution. models.
All the studies thus could not unanimously declare the best [A]. Analysis of peak traffic
option between the classic Poisson model and the exponential We plot the graph of total traffic offered in erlangs at each
model for telephone traffic in cellular networks. In contrast, cell site. We had considered scale is discrete with one hour
they suggested that call arrivals and holding time distribution intervals to find the number of peaks occurs during the 24
may be significantly time-correlated, due to congestion, user hours intervals. Next, we have calculated the average traffic
mobility and possible correlation between neighboring users. load, peak hour load and the peakdness factor to find the traffic
variation and peakdness range for given number of channels. In
Study of all previous work lead us to further investigate the
our calculation, peakdness factor has been defined as
exact correlation of recent mobile traffic behavior with classic
models and to check whether the traffic characterization
obtained would follow the previous behavior and models. Also,
as a step ahead, if classical models are applicable as best fit, Ideally the value of peakdness factor lie within the range of
then the extent of percentage variation applicable for actual 1 to 5 [16].Greater the range of peakdness factor means that
traffic data. server is over utilized and there may be chance of call drop.
III TRAFFIC CHARACTERIZATION AND ANALYSIS OF TRAFFIC Total traffic characteristics depend upon actual traffic load
DATA SETS. carried by the server. This carried load consist of traffic other
In a Mobile network, traffic refers to the accumulated then voice service like SMS originated; which also affect the
number of communication channels occupied by all users. For utilization of server performance. As a result it is important to
evaluate the rate of the SMS service to predict the behavior of
each user, the call arrivals can be divided into two categories:
mobile users along with performance. Also, now a days,
incoming calls and outgoing calls. Since every incoming call several companies offer bulk messages delivery in slack hour at
for one user must be originated from an outgoing call of very cheap cost. As a result, number of users may use this
another user, we only need to consider outgoing calls from service at redundant which may affect the quality of the voice
each user when we analyze the network traffic. Therefore, if service provided by the operators.

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The increasing competition may also motivate the operators to
compromise the voice service quality and as a result there may
be increase in call drop rate. To find the exact traffic, we must
consider the nature of SMS service used by the mobile users.

Fig 2.Call Arrival pattern for ideal Poisson distribution
The fig 2 shows the call arrival pattern of practical data
with arrival rate of 20 at a particular hour. The graph has been
extended to predict probability distribution of arrival of 37 calls
during the hour with mean arrival rate of 20.
The following relation was used to draw the graph-
Fig 1.Actual Carried traffic (in erlangs) and No. of SMS originated at
each hour.
Fig 1 shows the No. of SMS generated at each hour along
with carried traffic load. It shows the correlation between the
maximum Number of SMS generated and actual traffic (voice)
load to match with peak hour traffic or during slack hours. Where both mean and variance is equal to λT
From this observation, we can find the exact No. of TCH
(Traffic channels) and SDCCH (Stand alone Dedicated control
Channel) Channels require to serve the given traffic load.
[B]. Verification of Poisson Model
In this section we examine the relevance and verification
of Poisson Model. As discussed above, the Incoming call
arrival rate follows the traditional Poisson distribution where
the call arrivals in one second have to be perfectly uncorrelated
with the Call arrival in other seconds [17].For this analysis, the
arrival rates of incoming calls have to be determined from the
collected data sets and tried to correlate with Poisson
distribution model. The arrival rate of calls is λ (t) and it has
pseudo periodic trend for both the urban and rural area and are
found approximately same at two different days. The
probability distributions for actual call arrivals plotted against
Ideal Poisson arrival in one peak hour has been shown in fig 3
and corresponding percentage variation between the ideal and
actual pattern are shown in table2.

Fig 3. The distribution of call arrival with Ideal arrival rate

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[C]. Analysis of Inter-arrival traffic behavior The exponential variation of holding time included the
property of Normal distribution. We have traced the busy hours
In all previous work Interarrival (time between of each cell to get maximum number of calls for a correct
successive calls) rate are characterized and best fitted by assessment of holding time distribution. The pattern obtained
exponential distribution model. We plot and analyzed the closely follows the normal distribution pattern. Therefore we
graph of successive arrival call time of peak hours and adapted normal distribution for characterizing the holding time
compared them by fitting into the exponential models. along with peaks duration occurring at the mean value of
The exponential model for inter-arrival rate are characterize distribution and deviation factor (variance) to shows the actual
by [16] nature of channel holding time.

λt The probability distribution function of f(x) of normal
= λ. e distribution are define as
Where λ represent the arrival rate of calls
The Sample inter-arrival exponential model of peak hour are
obtained from a actual data sets of cell id -15231A

By using the normal distribution we plot and analyzed the
characteristics of holding time distribution of peak hours.

Fig. 3 Inter-arrival Graphical Analysis.
In Fig 4 the pattern obtained can be easily analyzed and
compare with standard (exponential) model to give actual idea
about the variation of real time traffic characteristics. Here the
2
value of R (.98) shows the error or variation of real pattern
with respect to Standard model.

[D]. Holding time distribution.

The most important parameter in any cellular traffic
analysis is holding or service time distribution .Generally; in
Common it is characterize by negative exponential distribution. Fig 4.Actual Holding time Distribution.
Mathematically, it’s shows that there is larger number of calls
of small duration as compare to the longer duration. The ideal As seen from fig 4. The holding time characteristics do not
negative exponential models are represented by religiously follow the normal distribution. This is because as
Shown from previous observations that the maximum number
− λ t of calls (in peak hours) does not contribute maximum traffic
P (t<T) = e i.e. holding time is larger during slack hours which support the
normal distribution in part.
Where λ represent the call arrival rate

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IV. RESULT AND DISCUSSION: Table 2. Analysis of Call arrival,Inter-arrival and the Call
holding time distribution (10 sample cells).
After analysis of all the 25 cells data recorded on 14 Jan
and 20 Jan 2011 and calculations made there after, average
traffic and peaked ness factors were calculated; results of such Call Intera Call Holding Time
10 sample cells shown in the table 1. Arri rrival (Normal distribution)
val error Mean Mean %
Table 1. Peak hour analysis of Sample Cells for SMS S % rate Value Dev Short
. Cell varia ref in sec in sec Durat
S
.
CELL ID PEAK
TRAFFI
PEAK
DENE
AVERGE
TRAFFI
PEAK
SMS
NO OF
SMS
N
o
ID tion Exp
model µ σ
ion
Calls
. (R2)
N C HOUR SS C IN HOUR
O VALU ERLANG
. E 1 M1170 193.73
2B 11.25 0.957 301.86 69.64
1 M1170 7pm-- 2.12 1.41 7pm- 23
2B 8pm -8pm 2 M1141
2 M1141 8pm-- 2.74 1.52 7pm- 115 3C 5.02 0.974 264.75 424.91 75.43
3C 9pm -8pm 3 M1013
3 M1013 9pm-- 3.50 1.56 9pm- 72 2F 11.76 0.982 293.76 403.37 71.64
2F 0pm - 4 M1003
10pm 2D 12.42 0.982 329.46 415.92 73.07
4 M1003 9pm-- 2.37 0.67 00am 46 5 R15751
2D 10pm --1am J 5.86 0.989 205.41 416.95 76.36
6 R15401
5 R15751 2pm-- 2.43 1.29 2pm- 33 T 19.65 0.968 167.21 258.82 73.04
J 3pm -3pm 7 R15451
6 R15401 7am-- 1.96 2.72 4pm- 16 A 13.45 0.985 275.56 382.97 70.90
T 8am -5pm 8 R15521
V 18.12 0.996 108.09 236.39 75.18
7 R15451 7pm-- 2.56 1.64 7pm- 48 9 R30071
A 8pm -8pm X 7.38 0.997 167.73 211.64 72.54
8 R15521 6pm-- 2.60 4.68 2pm- 134 1 R15301
V 7pm -3pm 0 W 6.09 0.967 140.55 212.67 73.77
9 R30071 7pm-- 2.19 3.24 7pm- 19
X 8pm -8pm
Analysis of the table and Graph of Call arrival pattern of all
1 R15301 7pm-- 2.62 0.88 7pm- 16 cells at peak hours , it is found that the arrival rate
0 W 8pm -8pm approximately follows the Poisson models with a percent
variation between 5- 20 with respect to ideal Poisson nature
The Result Shown in table 1 verified that there is more than
for a given probability. This Conclusion is estimated by
one peak hours occurs in a day with designated busy hour
assuming the variable arrival rate of different cells. However
occurs between 7am-10am in morning and late in evening
there may be chances of more than 20 percent variation occurs
between 6pm-9pm for different cells. At the same time we also
due to very high variable arrival rate but still the applicability
find that peakdness is nearly in the range of 2 to 5 which
of poison model found perfectly with given variable arrival
establishes that the peak traffic to average traffic ratio vary
rate as compare to other models.
nearly in large range. Another important result we find (from
the graph and table) of SMS behavior of users that; in more As obtained in table 2 and graph the Graphical
than 60 percent cases; the number of SMS in busy hours are representation of the inter-arrival pattern follows nearly the
actually high as compared to other times. This is a major reason exponential models. The Critical examination of each cell at
of higher call drop in peak hours when channel measurement peak hours reveals a variation between 0.01 to 0.10 with
reports are not available to BSC due to long messages. respect to ideal models.

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AUTHOR PROFILE International Expert through Commonwealth Telecom
Abhishek Gupta received his B.E. Organisation London during August 2010. He had also
degree in Electronics and delivered a speech on WiMAX coverage Evaluation at
Telecommunication Engineering International Conference on Advanced Communications
from Gyan Ganga institute of Technology 2011 at Seoul, Korea and chaired a session on
Science and Technology Jabalpur Network Management. He had also delivered speech on
(M. P.) in 2008. Currently, he is ADSL at International Telecommunication Union seminar in
pursuing his M. E. from the 2000 at Bangalore, India.
Department of Electronics and From 1997 to 2002, Dr. Manna has worked as Deputy General
Telecommunication Engineering, Manager in a Telecommunication Training Centre of DoT. He
Govt. Engineering College was first to install live training node for Internet Service
Jabalpur.His research interest Provider (ISP), designed training schedules and prepared
includes Computer networks and handbook and lab practice schedules. He had conducted
Future generation in mobile training programs for 5 batches of participants deputed by
communication System. Asia Pacific Telecomm unity (APT) and 3 more exclusive
batches for Sri Lankan Telecom. He had also conducted
several seminars with international experts through
Bhavana Jharia received her B.E. UNDP/ITU projects. In 2000, he had delivered distinguished
degree in Electronics and speech on ADSL in a seminar organized by ITU. During 1995
Telecommunication Engineering and 1996, Dr. Manna was posted in Telecommunication
from Govt. Engineering College Engineering Centre (TEC) and developed Artificial
Jabalpur (M. P.) in 1987. She did Intelligence (AI) based software for E10B telephone
her M.E. (Solid State Electronics) exchanges named E10B Maintenance Advisor (E10BMAD).
from University of Roorkee, Dr. Manna had worked as Development Officer in WEBEL
Roorkee in 1998 and Ph.D. (VLSI (erstwhile PHILLIPS) Telecommunication Industries during
Technology) from I.I.T. Roorkee in 1983-1984 after which he joined DoT and worked in different
2005. She joined the Department of executive capacities up to 1994.He was awarded National
Electronics and Telecommunication Engineering, Govt. Scholarship in 1973 based on school level examination and
Engineering College Jabalpur (M. P.) as faculty in 1990, silver medal for performance in college. He had both
where at present she is working as an Associate Professor. She graduated and post graduated in Radio Physics and Electronics
has 25 publications in National, International referred Journals Engineering from University of Calcutta and undergone
and Conferences. Her research interests are in Electronics trainings at Beijing University of Post and Telecom China in
Design and Simulation and Low Power VLSI Technology. 1990 and DARTEC, Montreal, Canada in 1999.
She is a member of IE (I), CSI, VLSI Society of India, senior
member of IACSIT and Life Member of ISTE.

Dr. Gopal Chandra Manna is
working as Senior General
Manager (Head Quarters),
Inspection Circle, BSNL, a wholly
owned Company under Department
of Telecommunications (DoT),
Govt. of India. Dr. Manna has
carried out extensive research on
coverage issues of GSM, CDMA,
WCDMA and WiMAX radio
access. Study of Wireless Traffic
and QoS estimation of Cognitive Radio are his current areas of
research. In Addition, he has written several articles on
advanced telecommunications which has been published in
national and international journals and symposiums. Dr.
Manna is regularly invited as a panel expert, invited speaker,
session chair etc. in seminars and conferences.
Dr. Manna has developed and conducted one week course on
Quality of Service Monitoring at Information and
Communication Technologies Authority, Mauritius as

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