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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 email@example.com firstname.lastname@example.org email@example.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 . 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 , 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 . 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; 61 http://sites.google.com/site/ijcsis/ 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 . 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 . 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 . 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 . 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  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 .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. 62 http://sites.google.com/site/ijcsis/ 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 .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 63 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, 2011 [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  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 64 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, 2011 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. 65 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, 2011 From the table 2 it is found that the channel holding time  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.  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  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  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. 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Vol. No.7, No. 9, September 2008. 66 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 7, 2011 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 67 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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