Simulation of 3g Networks in Realistic Propagation Environments

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A. Zreikat, K. Al-Begain: Simulation of 3G Networks in Realistic Propagation Environments

Simulation of 3G Networks in Realistic Propagation Environments

Aymen I. Zreikat and Khalid Al-Begain,Mobile Computing and Networking Research Group,

Department of Computing, University of Bradford, BD7 1DP, Bradford, UK

A.I.Zreikat, K.begain @bradford.ac.uk

Abstract

The coverage of a mobile system depends significantly on the geographical nature of the covered area. The signal

propagation can be dramatically different in downtown area with many high buildings than in a building free area. This

is particularly critical in third generation (3G) mobile systems based on Code-Division Multiple Access (CDMA) air

interfaces where the power management is a core part of the call admission control of the system. Therefore, performancestudies of such systems based on free space assumptions may lead to optimistic results. In this paper, the performance

of a 3G UMTS mobile network covering an urban area and surrounding suburban areas is considered. For modelling the

propagation, the COST-231 extended Hata model has been used which represents more realistic propagation models for

urban-suburban environments. Based on this model, closed expressions have been derived for the capacity bounds in the

existence of interference due to non-ideal orthogonality of codes in the used CDMA system and background noise. These

expressions are used to develop a network level sophisticated call admission control (CAC) algorithm to achieve nearly

equal blocking probability and balanced utilization over the whole network area. Detailed simulation is used to study the

performance of the network under different traffic and interference conditions. The results show that the proposed CAC

algorithm performs very well in achieving equal blocking probability by releasing the load on the heavily loaded central

area and, thus, achieving better balanced load on the network under different interference conditions. Additionally, some

design and environment parameters are studied like the height of the base station and the average height of the mobile.

Keywords: 3G Mobile Networks, Propagation Models, Capacity Bounds, Performance Evaluation.

1 Introduction

Coverage and capacity optimization have always been hot

research topics in the Third Generation (3G) mobile net-

works. The dynamic nature of the capacity stems from

the characteristics of the physical air interface which

uses the Code Division Multiple Access (CDMA) con-

cept [1][2]. Therefore, the traditional static call admis-

sion control (CAC) alogirthms that were suitable for 2G

mobile networks (For example [3]) are not applicable to

3G networks like the Universal Mobile Telecommunica-tion System (UMTS) [2].

In CDMA systems like UMTS, the scarce resource is the

transmission power. Given the Frequency Division Du-

plex mode (FDD) of the UMTS, the power budget of

the uplink and downlink are independent of each other.

The power of the uplink is limited by the transmission

power of the user equipment (UE) while the power bud-

get of the down link depends only on the capabilities of

the Node B (Base station). Furthermore, the Wide band

CDMA (W-CDMA) used in UMTS uses a set of spread-

ing sequences or codes with optimal correlation charac-

teristics to separate user connections. The interference

of the different signal depends on this correlation and in-

creases with the increase of the number of multiplexed

data streams. Since most services require a given signal-

to-noise rate, the number of admitable data streams will

be fewer than number of available codes [11][4]. As a

result, many CAC algorithms were proposed in the liter-

ature that are based on either admitted power level ([10],

[9], [12]) or on the value of the Signal-to-Interference Ra-

tio (SIR) ([18],[19]). In [5], the authors proposed a new

CAC algorithm based on capacity bounds of the UMTSsystem due to both interference and limited transmission

power of the UE. This algorithm was extended in [7] to

a multicell CAC using the soft handover feature of the

UMTS network. In the later, the new connection request

will be transferred to one of the accessible neighboring

Node B-s if there is no available capacity in the near-

est Node B or if the admission of this new connection

will disturb any of the existing connections unless this

connection can be accommodated in another cell. The

CAC algorithm aims to uses the soft handover feature of

the UMTS systems to provide multiple goals: (i) provide

I.J. of SIMULATION Vol. 4 No. 3&4 21 ISSN 1473-804x online, 1473-8031 print


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efficient utilization of the available capacity, (ii) protect

the QoS of existing connections, and (iii) prevent the loss

of coverage resulting from the so-called Cell Breathing

[11].

The main problem with the performance evaluation pre-

sented in [7] is that it assumes free space propagation

within the investigated area. This assumption is usu-ally not realistic and leads to optimistic prediction of the

system performance. In reality, the propagation of the

transmitted signal depends largely on the geographical

and building intensity of the area ([15],[20]). In this pa-

per, the same CAC algorithm of [7] is implemented in

a UMTS network of 7 Node B over an area that repre-

sent and medium size town with a highly built city center

and surrounding less built suburban area. For this sake,

the COST 231 Urban-Suburban propagation models [6] is

used which is an extended version of the Hata model [16].

In a previous work[8], the authors have derived capacity

bounds for different propagation environments includingdense urban, urban, suburban, rural in addition to the free

space environment.

The paper is organized as follows. Section 2 introduces

the investigated environment by defining the used propa-

gation model and summarizes the capacity bounds as the

maximum number of users and maximum distance cov-

ered by the Node B in both urban and suburban environ-

ments. In Section 3, the CAC is introduced. Section 4,

then, defines the simulation settings and the numerical re-

sults of the investigation before some concluding remarks

are given at the end of the paper.

2 The Investigated System

2.1 Basic assumptions

The investigation of this paper is based on a cluster of

UMTS mobile network comprising 7 Node B stations

(7 cells as shown in Figure 1 ). In [7] the Call Ad-

mission control algorithm is presented in a network of 7

cells where the ideal free propagation model is assumed.Whereas, in this paper, different propagation environ-

ments (urban, suburban) have been assumed using the

extended Hata model,[8]. The middle cell is the urban

(hot spot) area and the 6 surrounding cells are the sub-

urban ones. It is assumed that every UE will be softly

connected to the three nearest Node B-s, but the actual

data transmission will take place through one at a time.

The term Softly means that the UE and Node B are

exchanging signaling information but no resources are

allocated to the connection initiated by the UE unless a

proper CAC procedure has taken place. Although the

work is going on the multi service case, this paper will

concentrate on the introduction of the CAC algorithm for

single service case. For this service class, we introduce

the Service Factor, as where SF is

the spreading factor and SNR is the minimum Signal-to-

Noise ratio required for this service. It is assumed that

each Node B can ideally serve connections at service

factor .

d3

d1

d2

Suburban

Suburban

SuburbanSuburban

Suburban

Suburban

Urban

Figure 1: A seven cell structure in a macro cellular sys-

tems

The actual capacity and the coverage of each cell within

the network depends strongly on the interference levels in

the cell. The interference stems from some basic noise,

, and the interference from the non-ideal orthogonality

of the codes in the used CDMA system. Let denote this

non-orthogonality factor.

In this investigation, we do not consider mobility as it

makes the introduction of the algorithm much more com-

plex. This matter will be the subject of later work.

2.2 Capacity bounds

In this section, the capacity bounds for urban-suburbanenvironments are introduced,[8].

2.2.1 Extended COST-231 Hata model

The original Hata model was published in 1980 by Masa-

haru Hata [16]. Hata took the information in the field

strength curves produced by Yoshihisa Okumura [17] and

formed a set of equations for the path loss. The gen-

eral Hata model has two limitations. It has a limited path



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length and a limited frequency range. Therefore, a num-

ber of modified models have been produced to extend the

path length and frequency range in order to cope with the

requirements of the new technology.

The Hata empirical model uses a propagation equation

split up into two terms. A term that has a logarithmic de-

pendence on distance, , and a term that is independent

of distance. The Hata model also includes adjustments tothe basic equation to account for urban, suburban, dense

urban, rural propagation losses.

The general propagation loss in dB is given by [6]:

(1)

Where,

is a propagation loss in environment of type , in dB.

is the frequency of the transmission in MHz.

is the height of base station or transmitter in meters

(30-200m).

is the height of the mobile or receiver in meters (1-

10m).

is the distance between the receiver and the transmitter

in kilometers (1-20km).

mobile antenna correction factor.

is the correction factor which has different value for

each environment.

As can be readily seen, the path loss in the free space

model depends only on the frequency and the distance.

Whereas the other propagation models further parameters

are introduced such as : the height of the mobile ( ),

height of the base station (

).

Note that, the above general formula (1) has been given

for urban environments. However, the formulae for

other environments can be obtained from this formula by

adopting the suitable correction factors.

2.2.2 Extended COST-231 Hata model for urban en-

vironment

From (1), the urban model is defined as :

(2)

Where:

(3)

and

2.2.3 Extended COST-231 Hata model for suburban

environments

From (1), the suburban model is given by :

(4)

Which means that the Hata propagation model for the

suburban can be written as:

(5)

Where:

(6)

and

2.3 Capacity bounds for urban environ-

ment

In all the proposed models we have a relationship of the

form [15]:

(7)

where

is the received power,

is the transmission power,

is a function of

, the height of the mobile and ,

the height of the base station, and the frequency, f.

is the environment type, 0 for urban and 1 for suburban

is the distance between the mobile and the base station,

is the frequency in MHz,



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Clearly from (7), the distance, can be defined as:

(8)

The complete derivation of the extended Hata model foreach environment is in the Appendix at the end of this ar-

ticle.

The propagation loss for the extended Hata model in an

urban environment in the PCs (Personal Communication

services) range is given by:

(9)

The values of

for each propagation envi-ronment are given in (10, 11.

(10)

(11)

According to the above equation: (8), (10), (11)

-The formula for the maximum distance between the UE

and Node B can be defined as:

(12)

-The uplink capacity of the UMTS cell can be defined as:

(13)

3 CAC Algorithm

The CAC algorithm is implemented in a network of 7

cells. Let

denote the numbers of existing ac-

tive connections where

denotes the number of active

users in cell ,

. In the case of a new UE

appearing in the network, the UE must perform a regis-

tration phase before being able to request resources for

actual transmission of information which is controlled by

CAC algorithm.

The CAC algorithm aims to:-

1. Provide a required QoS for admitted connec-

tions by not allowing more connections than

the network can serve efficiently.

2. Protect existing connections from being dis-

turbed because of the admission of a new con-

nection.

3. Distribute the load over the network in an

efficient way by transferring the new calls

or even some existing connections from the

heavily loaded cell to those with lighter load.

4. Avoid coverage loss due to the so-called cell

breathing; i.e., when the coverage of more

than one neighboring cell shrinks below a cer-

tain limit.

Registration phase:

(a)- The UE measures the signal power of all ac-

cessible Node B-s.

(b)- The UE selects the 3 best signals and softly

registers at these Node B-s. Let

, and

denote the distances to these Node B-s in ascend-

ing order. The information (cell-number, received

power level) for these 3 stations are stored in

UE (as we do not consider movement, otherwise

the power levels should be measured and the list

should be updated dynamically). The UE will at-

tempt to connect to the one of these three Node Bin the same order and the connection will only be

rejected if it is rejected by all three Node B-s. The

CAC algorithm works as follows:-

The Call Admission Control algorithm:-

Assume that Node B is the closest with distance

to the new UE asking for connection (In other

words, the UE falls into the coverage area of cell

). Therefore, the UE will try first to get admission

in this Node B as follows:

1. For cell , IF

then

goto the Step labeled REJ, otherwise con-

tinue with next step.

2. Calculate

as shown in equation

12 above.

3. IF

then gotoREJ, other-

wise continue with next step.

4. Calculate

for all other cells

.



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5. For all neighboring cells to cell , (

) check :

IF

, where is the distance between Node

and Node

then gotoREJ otherwise con-

tinue with next step.

6. For cell , check for all existing active con-

nections

, IF for any connection

the distance

then goto

TRANSFER, otherwise gotoACCEPT.

7. TRANSFER: is a procedure to transfer an

active connection to another cell to which the

UE is softly connected. Therefore, this pro-

cedure is a complete replica of this CAC al-

gorithm except for the TRANSFER in order

to avoid an infinite loops. If the TRANSFER

of Connection is successful then goto AC-

CEPT, otherwise gotoREJ.

8. REJ The connection cannot be admitted tocell , therefore

(a)- , that is try with next cell (Node B),

(b)- IF

then Goto to Step 1 otherwise

gotoREJECTconnection.

9. ACCEPTconnection of the UE in cell .

GotoEND.

10. REJECTconnection of the UE.

11. END

4 The Simulation

4.1 Traffic model

As mentioned earlier, the studied system comprises 7 cell

where the central cell is heavily built area (Urban prop-

agation model used) and the surrounding cell are mod-

eled with Suburban propagation model. The arrival pro-

cess over the whole network is assumed to follow a Pois-

son process. The traffic is assumed to be uniformly dis-

tributed over the coverage area of each Node B. Two dif-

ferent traffic patterns are considered in this paper: ho-mogeneous and hotspot. In the homogeneous case, the

load is equal for all cells. In the hotspot scenario, we as-

sign a double load of the calls to cell number one which

is in the center of the network while the other six cells

have the same load. The latter is suitable for modelling

a metropolitan area where the central Node B serves the

city center area. The location of the user in the cell is

chosen randomly and then the absolute coordinates of the

user is determined by the Node B. The call duration is

assumed to be exponentially distributed with mean

and the user leaves the system as soon as the call ends.

This assumption is not realistic for packet switched net-

works but if we consider the system operation at burst

level where we assume that a group of packets will be

buffered for transmission in the UE before initiating the

call admission procedure , then this assumption can be

acceptable.

Parameter Symbol Values

Number of codes N 64

Radius of the cell r 578.03m

Spreading factor SF 64 chips/symbol

Service factor S 32

Signal to noise ratio SNR 2 db

Max. transmission rate

125 mW

Basic noise

-80 dBm

Interference factor 0.30, 0.50, 0.70

Wave length 0.15m

Height of the mobile

2m,5m

Height of the base station 50m,100m

Table 1: Simulation Parameters

4.2 Numerical results

4.2.1 The effect of the interference factor, ( )

The system has been studied via a detailed simulation us-

ing the parameters as shown in Table 1. The investigation

will concentrate on the most important performance mea-

sures, the blocking probability and the utilization versus

the call rate. All the result obtained in this paper were set

to 95% confidence intervals.

The performance of the system in such environment can

be influenced by many factors including the interference

levels, the physical characteristics of the network, like the

height of the Node Bs and the height of buildings in the

area. In this paper, we present only results that show the

capability of the proposed CAC algorithm on achieving

equal blocking probabilities over the whole area of the

network by balancing the load even in different interfer-ence conditions and different traffic loading (the hotspot

scenario). In this investigation, we set the Base station

height (average mobile height) in the urban and suburban

areas to be 100m and 50m, 5m and 2m, respectively.

Figures 2 - 7 show results grouped into three sets accord-

ing to different values of interference factor =0.3, 0.5,

0.7, respectively. Each set consists of two figures for the

blocking probability and cell utilization. The utilization

in the this case is calculated as the proportion of usage of

the available bandwidth (codes).



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[8] Aymen I. Zreikat, Khalid Al-Begain and Kevin

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Biographies

Aymen I. Zreikat obtained his BSc in

Computer Science from Yarmouk University, Jordan in

1990 and MSc in Computational Engineering from Uni-

versity of Erlangen, Germany in 2000. In Jan, 2001, he

joined the Mobile Computing and Communications Re-

search Group in the Department of Computing of Brad-

ford University, UK as research/Ph.D. student where he

is in his final year. His area of research is in the Per-

formance Evaluation and Resource Management of 3G

Mobile networks in which he has a set of journal and con-

ference publications in this field.

Khalid Al-Begain received his High

Diploma (1986), the Specialization Diploma of Com-

munication Engineering (1988) and his Ph.D. degree in

Communication Engineering (1989) from the Technical

University of Budapest in Hungary. From 1990, he held

the position of a Assistant Professor at the Deptartment

of Computer Science of the Mutah University/Jordan. In

1996,he became an Associate Professor at the same uni-

versity. In 1997 he moved to the Department of Com-

puter Science at the University of Erlangen-Nuremberg

in Germany as Alexander von Humboldt research fel-

low. Furthermore, he spent one year as Guest Professor

at the Chair of Telecommunications, Dresden University

of Technology, Germany. From 2000 to 2003, he has

been Senior Lecturer and Director of Postgraduate Re-

search in the Department of Computing of the University

of Bradford, UK. He is currently Professor in the School

of Computing in the University of Glamorgan, Cardiff,

Wales and the head of Mobile Computing and Network-ing research group. He co-authored the book Practi-

cal Performance Modelling published by Kluwer Aca-

demic Publishers and more than 60 journal and confer-

ences papers. He is senior member of the IEEE and many

other scientefic organisations. He also serves as Guest

Editor for a special issue of this journal on Analytical

and Stochastic Modelling Techniques and as Conference

Chair for the ASMT03 to be held in Nottingham,UK in

June 2003. His research interests are performance mod-

elling and analysis of computer and communication sys-

tems, analytical modelling and design of wireless mobile

networks.


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Simulation of 3g Networks in Realistic Propagation Environments