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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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                                                                                                      ISSN 1947-5500
                                                               (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                         Vol. 9, No. 7, 2011

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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                                                                                                        ISSN 1947-5500
                                                                  (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                            Vol. 9, No. 7, 2011



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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                                                                                                           ISSN 1947-5500
                                                              (IJCSIS) International Journal of Computer Science and Information Security,
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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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                                                                                                     ISSN 1947-5500
                                                            (IJCSIS) International Journal of Computer Science and Information Security,
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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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                                                                                                    ISSN 1947-5500
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From the table 2 it is found that the channel holding time                [6] Roch A.Guérin, “Channel occupancy time distribution in a
closely follow normal distribution in peak hour observation of            cellular radiosystem”, IEEE Transactions on Vehicular
the day. This is evident due to good number of calls with                 Technology, vol. vt-35, no. 3, August 1987.
duration nearly zero (below the mean value µ of duration)
i.e. Short calls are observed more then 70 percent of total calls.        [7] Daehyoung Hong, Stephen S. Rappaport, “Traffic model
Short channel holding time can be attributed to the user’s                and performance analysis for cellular mobile radio telephone
behavior and the operator commercial policy.                              systems with prioritized and nonprioritized handoff
                                                                          procedures”, IEEE Transactions on Vehicular Technology,
V. CONCLUSION                                                             vol. vt-35, no. 3, August 1986.

   After analyzing all cell data it is stated that if issues like         [8] Francisco Barcelo, Javier Jordan, “Channel holding time
congestion, handover calls etc are neglected, the Poisson                 distribution in cellular telephony”, Electronics Letters, vol. 34
model is still adequate to describe realistically telephone               no. 2, pp. 146-147, 1998.
traffic in cellular networks also. The Call holding time is
better fitted in normal distribution with appreciable number of           [9] F. Barcelò and J. Jordan, “Channel holding time
short calls below mean whereas long calls are fewer in                    distribution in public telephony system (PAMR and PCS),"
number. The variance observed longer than mean value which                IEEE Trans. Veh. Technol., vol. 49, no. 5, pp. 1615–1625,
indicates area covered by the cell is of mixed locality with              Sept. 2000.
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establishes that there is more need of traffic channels to handle         distribution of channel holding time in cellular telephony
the traffic load and control channels to meet SMS load at same            system," in Proc. IEEE Veh. Technol. Conf., May 1996.
time in order to meet given quality of service requirements.
                                                                          [11] Y. Fang, “Hyper-Erlang distributions and traffic
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performance.
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                                                            (IJCSIS) International Journal of Computer Science and Information Security,
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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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