Traffic Modeling for Capacity Analysis of Gsm Networks in Nigeria

7/29/2019 Traffic Modeling for Capacity Analysis of Gsm Networks in Nigeria

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Continental J. Information Technology 4: 78 - 89, 2010 ISSN: 2141 - 4033

Wilolud Journals, 2010 http://www.wiloludjournal.com

TRAFFIC MODELING FOR CAPACITY ANALYSIS OF GSM NETWORKS IN NIGERIA

Biebuma J.J.,Orakwe S.I and Igbekele O.J

Department of Electrical/Electronic Engineering, University of Portharcourt, Portharcourt, Rivers State, Nigeria.

ABSTRACT

A precise analysis of mobile users behaviour in terms of mobility and traffic would help to optimize

capacity for both circuit and packet switched services. This research work employs the multiplicity of

techniques for the capacity analysis of GSM network in Nigeria. Enhanced stochastic knapsack was

evaluated for resource sharing approach in multi-services. Erlang Loss Model was adopted for SMS

capacity analysis. The offered traffic that is Lost Traffic based was used to dimension the system

resources. This was made possible by the characterization of a typical representation of the Northern

part of Nigeria. Finally different frequency hopping types were compared, and the novel power based

variant DFH was considered for improved spectral efficiency.

KEYWORDS: Traffic, Modeling, Multiservice, Knapsack, stochastic, analytical approach

INTRODUCTION

In spite of large amount of studies about GSM capacity analysis appeared last half decades, a number of issues

remain open. The one of the most important issues among them is anticipating the traffic intensity for proper

dimensioning of the network, especially for multiservices. Traffic modeling is the critical part of networks

modeling, it is the key point on performance evaluation for any communication network. Traffic Model can be

grouped into two series, namely, smooth and non smooth model. Smooth traffic model can be divided into twokinds: Short Range Dependence (SRD) and Long Range Dependence (LRD), (Riedi et al, 1997). Construction

of a traffic model is the trade off of the following factors: Fitting nature, number of describing parameters and

complexity of the parameter evaluation (Gabriel et al, 2004). The advent of Asynchronous Transfer Mode

(ATM) has renewed the interest in resource-sharing models with different resources requirements. Due to the

non homogeneity of the traffic and the difficulty of its characterization, trunk reservation is used for resource

contro (Altman et al, 2001). This work studies such a resource control to derive a traffic model to calculate the

number of resources that are required to handle peaks of traffic in the cell while meeting the grade of servicerequirements.

In classical GSM network dimensioning, when there was only one service with an associated blocking rate,

Erlang-B Law was a suitable traffic model. Erlang B traffic models have been developed for wire line networks.

They predict the aggregate traffic processed by the switches. Unlike a fixed network, a cellular telephonenetwork must support moving customers (Leung et al, 2004). Present GSM network provides mixed services ,

besides typical voice services, data services and multimedia services are in all kinds. For this reason, Erlang-B

Law is no longer suited to traffic modelling in GSM network (Peter, 2009). Enhanced Knapsack is a more

suitable multi-service traffic model, finding the system minimal capacity given the requested GoS, reproducing

the sharing of resources between different users and different services.

In most of the past techniques, it is assumed that the mobile network is homogeneous, the cells in the network

are studied one by one with stochastic models therefore simulation is used as the main modeling tool. In recentyears, a number of analytical frameworks were developed to obtain more general results and they are much

more useful than simulation studies this is because analytic results evaluate network performance under a wide

range of conditions (Hong et al, 2006).

Firstly, this work examines source, volume and type of data generation by analytical method, then investigates

the kind of channel allocation with respect to the frequency hopping types and proposes the best spectral

efficiency. Further GoS parameters in the multiservice GSM network are investigated for fairer source

allocation in the traffic mix environment.

GSM CAPACITY ANALYSIS AS IT RELATES TO NIGERIA

High demand of GSM services and limited capacity are major causes for congestion in GSM cellular systems in

Nigeria, it is also expected that congestion will continue in every peak usage hours. By congestion, we mean

that in some cells, there will be no more capacity left. More specifically, in a congested cell, there will be nomore available data channels for use by additional mobile hosts in the cell. However, the control channels for


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Biebuma J.J et al.,: Continental J. Information Technology 4: 78 - 89, 2010

signaling (or paging) may still be accessible by all mobile hosts in a congested cell (Kuboye, 2006). The

peculiarity in belief, culture, environment, economy, expertise, research and development centre, even the

technology itself, and many other factors have also greatly influenced the failure of good cellular service in

Nigeria.

The performance of GSM communication systems depends significantly on the mobile radio channel.

Propagation models predict the average signal strength and its variability at a given distance from the

transmitter. Different models exist for different types of environments (e.g., urban and rural) (Shenghui et al,

2007). This is the reason for management of calls blocking between the subscribers due to limited available

radio resources, thus increasing the spectral efficiency of GSM cellular system has become a great concern to

the GSM Network operators, particularly in Nigeria. The need to tackle the problem of congestion on the GSM

network is for the benefit of the operators and users as well as the vendor. This thesis is motivated by this need;

the present research offers solution to the above problem. Since Nigeria does not have a technology of her own,

all we need do is to adapt the adopted technology into our environment, and by adaptation, you have to be

analytical in your approach of data generation and processing, rather than being stochastic.

METHODS AND ENVIRONMENTCHARACTERIZATION AND DELINEATION

Our approach for characterization was experimented in 6 Northern States: Taraba, Adamawa, Gombe, Bauchi,

Yobe and Borno. The BSS captures of 3 BSCs per state were used. The covered areas were now delineated over

Yola and Numan which were representatives since they included all the characteristics of a typical Nigeria, as in

urban and rural for commercial, residential and highways.

Our modelling approach is based on the analysis of the OMCR captured real traffic data and using the RNP opt-

net software as a statistical tool to characterize the arrival processes and derive distributions to fit the measured

data. Opt-net software uses the statistical method of Kolmo-gorov Smirnov (K-S) goodness of fit test, using

maximum likelihood estimation to calculate parameters of the fitted distributions (Alcatel, 2007). We monitor

the BSS measurements over the radio network servers in order to propose a mobility model that constitutes a

systematic way to evaluate the radio performance and customer behaviour in the network. This study is divided

into two groups according to the population mobility and traffic characteristics. The graphical outputs of callblocking probability are produced useful for capacity analysis and estimation. The output Erlang is treated as

carried load and adapted into the lost and overflow traffic to obtain the offered traffic required for appropriate

cell dimensioning.

MEASUREMENTSThis research work started by capturing the traffic volume in 6 Northern states: Taraba, Adamawa, Gombe,

Bauchi, Yobe and Borno. The BSS captures of 3 BSCs per state were used. The traffic intensity in these

locations were taken over 24h for 48 weeks, stratified to work days and weekends. The mean hours of the traffic

in Erlangs was recorded and plotted. The traffic load, was taken as the carried traffic. Also, the average rate of

blocked calls against the generated Erlangs was used to find the Lost Traffic, to enable us formulate the OfferedTraffic for accurate cell dimensioning, so as to allow our novel model optimize effectively the resources

allotted. The covered areas were now delineated over Yola and Numan which were representatives since they

included all the characteristics of a typical Nigeria, as in urban and rural for commercial, residential andhighways. This data was carefully plotted on the graphs, as shown in the figs of the measurements below:

Fig.1: Traffic intensity versus the hour of the day ( Yola) Fig.2: Traffic intensity versus the hour of the day ( Numan)


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Fig.3: Average rate of blocked calls over the hour Fig.4: New arrival calls over handover calls

of the day

Figure 5: Illustrates the relationship between BP Figure 6: Illustrates the relationship between blockingwith load in (Erl) probability and users

Figure 7: illustrates the relationship Fig. 8 illustrates the relationship between the

between the spectral efficiency in (bit/s/Hz) spectral efficiency(bit/sec/Hz) and radius (m)

with load


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FIG.9: Spectral efficiency versus number of cell per cluster

Multi-service Traffic Model

This work develops a traffic model for a GSM network designed to meet heterogeneous service requirements of

different applications. For example it is clear that a voice call will require lower service rate than a movie

download. In such case, a movie download will belong to a class that require more channels. Many works have

been developed on multi-server single-service using M/M/K/K system, but in this work, a multi-service lossmodel is developed for enhanced Knapsack Traffic model, with special interest in the blocking probability of

each class of traffic. Unlike the case with M/M/K/K system where all customers experience the same blocking

probability, in this work, customer belonging to different classes experiences different blocking probabilities.

Consider a GSM call with 7 channels and assuming that five out of the seven channels are busy. If a customer

that requires one channel arrives, it will not be blocked, but if a new arrival, that requires three channels, will beblocked. Therefore, customer that requires more channels will experience a higher blocking probability.

Model Description

Consider a set of K channels, serving customer belong to I class. Customer from class I require simultaneous S

channels and their holding times are assumed exponentially distributed with mean . Class customer

arrival arrives according to an independent Poisson process with arrival rate . The holding times are

independent of each other, of the arrival processes and of the state of the system.

An admitted class- customer will use S channel for the duration of its mean holding time, , after all

which S channel are released to serve others. When a class- customer arrives and cannot find S free channel,

it is blocked and cleared from the system. The probability that an arriving class- customer is blocked is denoted

by B( ).

Model DerivationTo develop a multiservice Traffic model for an enhanced Knapsack of capacity C, the steady state probability of

process is used to satisfy the conditional probability of occurrence of state in Stochastic Knapsack.

Now, consider a set of K channels serving customers belong to I classes with traffic intensity each using

resources and a knapsack of Capacity C. Letting j = ( , ) represent the possible states of the Knapsack,

also consider the steady-state probability distribution of for the case K=

Let = Independent uni-dimensioned continuous-time Maxkor chamn

(t) = Number of Class customer in the system

Where = 1,2..I.

Characterized by the birth rate = I; death rate =

) = steady state probability of process (t)

Then ) satisfies the equations:


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Bvoice =B(V) = 1- P( ) = + = 0.26376264

The video call blocking is :

Bvideo =B(VD) = 1- P( ) = + + + = 0.58

Hence, the calls are only blocked when the system is completely full, therefore the

Bvoice = +

And the video calls are blocked also when there is only one channel free, the

Bvideo = + +

Table 1: Cell dimensioning parameters

Service Intensity Blocking/Pilling Request Resources

Speech

Video call DL275

10Er

1Er0.26 pu 26%

0.58 pu 58%

1TS

2 TS

SMS MODEL

Erlang loss system is fashioned to dimension the requirements for signaling of SDCCH, and denoted by S. The

streams are assumed to be Poisson, the blocking probability, B, for each of the three jobs is same and equal to

B(s, a). We also model re-attempts on blocking, by assuming that every time a request is blocked, it is re-

attempted with probabillity r. With re-attempts, the effective arrival rate, ef f of a stream which has arrival

rate of new requests = , is given by ef f = /(1 rB). This gives rise to a set of interdependent

equations, which can be solved iteratively until convergence is achieved.

The blocking probability for a message is then given by:

Bmessage = 1 (1 B)n

, where n is the number of fragments that a message is split into.

eff= c (1- r B)

Where,B = Blocking probability for the aggregate signally for traffic

I= intensity of aggregate traffic

r = probability of re-trial attempts

c = s + c + v

B (s, a) =

Where s = No of available SDCCH signaling

a = Traffic intensity

for SMS, Maximum size of a single sms message = 160 character = = 140 octet

Probability of SMS message = 1 (1- B)n

Where n = No offthe strings

Again,

s = No of SMS per cell per sec = =

Now consider 1Million messages in a system. No of sms per cell per sec:

= 10Hr

= 0.55

Assuming that the number of voice calls are about 12 times as many as SMS messages we have v = 6.6.

Assuming also that location updates are about 10% of this volume of SMS messages, giving us, l = 0.05 per

cell per second.

Since the GSM connect time requirement for voice calls is less than 4 seconds, I assume that the voice call


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Algorithm Principle

No Yes

Yes

Fig. 10: Enhanced Knapsack Model

CS

Blocking Rate

Erlang B/ Erlang C

Analogy PS service

Quantile &

delay

Capacity= C

Normalise Resources

C = 1

Cal blocking probability

with knapsack algorithm

taking in to account both

CS and PS traffic

For CS services, comparethe blocking probability

found with the reqd GoS

For PS services,translatethe blocking probabilityfound into PS GoS and

compare it to the rqd GoS

Increment C

All

services


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setup part takes 0.5 seconds, i.e., v1 = 0.5 on the SDCCH. In case of location updates, also assume an

additional 0.1 second of use of the SDCCH ,bringing the total to l1 = 0.6.

For the enhanced Knapsack model shown below, normalization resource is:

= x

Since Knapsack can handle a circuit switched service mix, then enhanced Knapsack model algorithm is

developed to handle both circuit and packet switched traffic. To overcome the blocking and sharing grade of

service issues, an analogy in the similarities between Erlang-B and Erlang-C formulas have been imagined. By

resource normalization factor, the capacity keeps adjusting until the grade of service requirements are met for all

services.

DISCUSSION

Using stochastic knapsack model as an extension of the Erlang-B algorithm to find the analytical of the

blocking probability for each service of voice and data, knowing the system capacity and the incoming traffic.

The performance of call blocking probability is drawn versus the traffic (A) as shown in Figure 5.

From Call Blocking view

This figure presents a comparison of blocking probabilities in GSM systems (without FH), (with FH) and (with

DFH).

It is seen from the graph the blocking probability of FFH at(load = 2) is equal to (0.07), is more than GSM-

WFH at( load = 2) is equal to (0.0). The blocking probability is suppressed considerably by applying (DFH). If

we compare the Frequency Hopping systems for a loading of (3), GSM (no- FH) has a blocking probability of

(0.0), where when with hopping is 0.08; and 0.082 for DFH.

As loading increases, blocking probability for GSM (no- FH) increases very fast, while for FH this increase is

considerably slow. For DFH the blocking probability is (0) from loading (6), whereas after exploiting DFH, nosingle user experiences outage until a very high loading value. It is seen from the graph In Figure (6), the

blocking probability of FH at user = (60) is equal (0.02), is less than GSM (no FH) at user = (60) and is equal

(0.03). The blocking probability is very low in DFH. If we compare between the systems for a loading of (60) at

(70), GSM (no-FH) has an outage probability of (0.09), where this probability decreases to (0.025) for FH and

(0.0) for DFH. As loading increases, blocking probability for GSM increases very fast, while for FH this

increase is considerably slow. For DFH the blocking probability is (0) until users number (70).

From the Spectral Efficiency technique:

From figure (7) it is seen that traffic control could potentially increase the spectral efficiency by decreasing the

traffic itself, since the reduced interference could allow the users to achieve higher rate. This figure shows theperformances of these systems GSM without FH (no FH), with FH, with DFH. Another striking result of Figure

(7) is the improvement in the average user spectral efficiency and hence in the high data rate coverage. Althoughfor low loading the difference in spectral efficiency is not much for load = 1, GSM-no FH (3.5 b/s/Hz), with FH

(5.1 b/s/Hz), with DFH (6.5 b/s/Hz), and the results show that with the help of the relays better with heavy

traffic for load = (2.3), GSM (no FH) ( 1.9 b/s/Hz), FH (2.2 b/s/Hz), DFH (2.9 b/s/Hz). At loading = 4, the

spectral efficiency for DFH is higher than that of GSM no- FH (1.05), FH (1.4). Figure (8) shows the

performance of Spectral efficiency with reuse distance (radius (m)). The Spectral efficiency is shown to begenerally increased by decreasing the reuse distance. From optimal value of spectral efficiency the optimal

value of reuse distance can be obtained, and from the optimal reuse distance also the minimum value of

interference can be obtained. From results shown in Figure (9), it is seen the spectral efficiency increases, when

the number of cell per cluster is decreasing. The optimal value (best the minimum) of the number of cell in

cluster causes reduced interference, since the reduced interference could allow the users to achieve higher rates.


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DISCUSSION SUMMARY

The result of SE with DFH is (6.5 b/s/Hz), compared with FH (5.1 b/s/Hz) and GSM (3.5 b/s/Hz). It leads to an

improvement in performance of system and how reducing interference could allow the users to achieve higher

rate. Blocking probability is improved because of handling speed drop in received signal level for call. Insystems of GSM, blocking probability increases very fast by increasing the number of users. In FH technique,

the blocking increases slowly at the beginning. In DFH blocking probability increases very slowly with

increasing the number of users. The received power method gives better performance than other methods of

DFH. Because the speed of DFH/Pr process is faster than that of the other method by factor T/6 (where T is the

processing time).

CONCLUSION

The enhanced Knapsack model has developed a reliable and accurate Traffic model, which allows us to:

1. Address resource sharing by users accessing different services2. Handle both circuit and packet switched services with different grade of service3. Derive an optimised capacity reflecting the real traffic occurring in the cell to anticipate capacity analysis.Also, based on the analysis conducted, the following conclusion could be drawn: the new developed variant ofDynamic frequency Hopping has shown the method that does not allow received signal power to drop. Therefore

the received power method gives better performance than other methods of DFH, this is because the processing

speed of DFH/Pr is faster than that of any other method by a factor of T/6, that is, this is six times faster than

other methods (where T is the processing time).

From the SMS capacity analysis, it is observed that the effect of increasing volumes and sizes of SMS messages on GSMnetwork can be significant. SMS uses a resource that is shared by other very important control messages

(especially, voice call set-up).

The analytical method of data for cell dimensioning and the Lost traffic approach allows more accurate optimal

modelling.

CONTRIBUTIONPractical experiences have been able to assist to identify some practical requirements of some past

reviewed literatures so as to have more realistic framework for modelling. Essentially the need for

multiplicity technique in a GSM network capacity analysis, is a novel discovery. The work was able to

develop different specific Grade of Service tools of call blocking probability for different services on

GSM platform. Also able develop a technique to increase the spectral efficiency using DynamicFrequency Hopping that is received power based. Since the practical experience has revealed that

homogeneous cell size and regular frequency reuse simply assume that traffic density is homogeneous, this

does not reflect at all the real networks where the number of mobile users under a particular coverage area is

random and time varying due to user mobility, I was able to identify and develop the model describing no

homogeneities of traffic distributions of GSM network.

RECOMMENDATION

The following recommendations can make the work in this paper of great practical usefulness: further researchwork that would develop a program tracking received signal level for determining the handover process and also

that would maintain the received signal power through communication processes is recommended for the

completion of this work. The implementation of these new techniques and tools should be implemented on thelive network to validate its accuracy.

FUTURE WORK

Another parameter that is important to consider for GSM network performance and capacity analysis is the

channel holding time. This can be defined as the time during which a new or handover call occupies a channel

in the given cell, and it is dependent on the mobility of the user. The accurate knowledge of the channelholding time probability distribution function is expected to produce more accurate analysis of traffic

modeling. This is expected to improve the efficiency of the radio resources and thereby make possible the

development of new services offered through GSM platform such as GPRS/EDGE.


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Zou D, X. Zhang, and W. Wang (2008), Muthservices Traffic Models of Heterogeneous Wireless

Communication Networks Technical Journal, 2008 (6): 11-18.

Received for Publication: 01/11 /2010Accepted for Publication: 02/12 /2010

Corresponding Author

Igbekele O.J

Department of Electrical/Electronic Engineering, University of Portharcourt, Portharcourt, Rivers State, Nigeria.

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Traffic Modeling for Capacity Analysis of Gsm Networks in Nigeria