NorLianMFKE2008-DU

INTERFACE DEVELOPING FOR HATA MODEL USING MATLAB

NOR LIAN BINTI MOHD NORDIN

A project report submitted in partial fulfillments of the

requirements for the award of the degree of

Master of Electrical (Electronics and Telecommunications)

Faculty of Electrical Engineering

Universiti Teknologi Malaysia

MAY 2008

iii

Especially dedicated to;

My beloved husband,

Azli B.Moh,

My son and daughter,

My mother …

Pn Sharifah Ain Bt Syed Rasdi

And in memory of my father…

En Mohd Nordin B. Hj Othman

&

To all my family

Thank you for your support

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ACKNOWLEDGEMENT

Highest praise to the almighty for giving the determination and ability to

complete this project.

First of all, I would like to take this opportunity to express my deepest thanks

to my supervisor, Prof. Dr. Tharek B. Abd. Rahman for his invaluable guidance,

encouragement and suggestion throughout this project. I’ve really appreciate the

knowledge and advises he generously share with me. His attitude in helping me to

successful my project are cannot be fully expressed.

I would like to extend my sincere thanks and appreciation to my colleagues

friends for their friendship, co-operation and encouragement during study.

Most of all, I would like to express my indebtedness to my family especially

my husband for their moral support, affection and encouragement in all my

undertaking.

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ABSTRACT

Mobile radio communications in cellular radio take place between a fixed

base station (bs) and a number of roaming mobile stations (ms). From the research

that have been taken place over the years, those involving characterisation an

modeling of the radio propagation channel are amongst the most important and

fundamental. The propagation channel is the principal contributor to many

problems and limitations the best mobile radio systems. One obvious example is

multipath propagation which is the major characteristic of mobile radio channels. It

is caused by diffraction and scattering from terrain features and buildings, that leads

to distortion in analogue communication systems and severely affects the

performance of digital systems by reducing the carrier –to-noise and carrier-to-

interference ratios. A physical understanding on mathematical modeling of the

channel is very important because it facilitates more accurate prediction of system

performance and provides the mechanism to test and evaluate methods to see the

effects caused by the radio channel. The main objectives of this project is to select

one of the propagation prediction model and used this model to develop an interface

using Matlab software. With this simulation, hope that this interface can be one of

the friendly interface to the user.

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ABSTRAK

Perhubungan komunikasi ‘handphone’ dalam sistem komunikasi radio adalah

komunikasi antara satu stesen tetap dengan beberapa lingkaran sel komunikasi

‘handphone’. Dari penyelidikan yang telah dilakukan, kajian mengenai faktor-faktor

yang mempengaruhi sistem perhubungan radio adalah yang paling penting dan

terkini. Saluran yang digunakan dalam sistem perhubungan radio adalah faktor yang

penting mempengaruhi kepada kebaikan dan keburukan sesuatu sistem. Salah satu

dari masalah yang timbul adalah daripada pelbagai isyarat yang terbentuk daripada

pantulan dinding, bangunan dan sebagainya yang membawa kepada perubahan pada

isyarat analog dan juga kesan dari sistem digital. Pemahaman yang mendalam

mengenai bagaimana isyarat dan saluran perhubungan itu dicipta perlu untuk

meramalkan atau memperbaiki lagi sistem perhubungan ke arah sistem yang lebih

berkualiti dan mampu mewujudkan satu mekanisma yang boleh dilakukan untuk

menguji sistem tersebut. Objektif projek ini dilaksanakan adalah memilih salah satu

daripada model yang digunakan dalam system perhubungan dan menggunakan

model ini sebagai antaramuka dengan menggunakan program Matlab. Moga

antaramuka ini akan menjadi satu antaramuka yang mudah dan senang digunakan.

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TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES x

LIST OF FIGURES xi

LIST OF APPENDICES xiii

1 INTRODUCTION 1

1.0 Project Objectives 1

1.1 Problem Statement 1

1.2 Thesis Outline 2

1.3 Wireless Communication 2

1.4 Radio Spectrum Classification 3

1.5 Propagation in free space loss 6

1.6 Summary 10

2 RADIO PROPAGATION MODELS 11

2.1 Introduction 11

2.2 Types of radio propagation 12

2.2.1 Indoor Attenuations 12

2.2.1.1 Physical Effects 12

2.2.1.2 Examples of Indoor Models 14

a) ITU Indoor Model 14

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b) Log Distance Path Loss Model 16

2.2.2 Outdoor Attenuations 18

2.2.2.1 Foliage Models 18

a) Weissberger’s Modeified Model 18

b) Early ITU Model 19

2.2.2.2 Terrain Models 20

a) Egli Model 20

b) Longley-Rice Model 20

c) ITU Terrain Model 21

2.2.2.3 City Models 22

a) Young Model 22

b) Okumura Model 23

c) Hata Model 27

d) Cost 231 Model 29

e) Cost 231 Walfish-Ikegami Model 30

f) Lee Model 33

2.2.3 Environmental Effects 37

a) ITU Rain Attenuation Model 37

b) Crane Model 39

2.3 Summary 39

3 SIMULATION USING MATLAB 41

3.1 Overview of Matlab 41

3.2 Why do I choose Matlab Software? 41

3.3 Graphical User Interface (GUI) 42

3.4 GUI works 43

3.4.1 Components 43

3.4.2 Figures 43

3.4.3 Callback 44

3.5 Creating and Displaying GUI 46

3.6 Summary 46

4 INTERFACE FOR HATA MODEL 47

4.1 Flow chart on how to make interface 47

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4.2 Layout for Hata Model 48

4.3 Set the properties of the button 49

4.4 M-File 50

4.5 Error Dialog Box 51

4.6 Interface for Hata Model 52

5 DISCUSSION 53

5.1 Conclusions and recommendations 53

5.2 Summary on some propagation model 55

References 56

Appendices A - C 59 - 74

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LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Measure of accuracy of simple model 13

2.2 Loss of signal power with distance 15

2.3 Floor penetration loss factor 16

2.4 Calculations of coefficients 10γ and σ in dB 17

2.5 Estimated values of ∆h 21

2.6 Relevant value for rooftop-to-street diffraction

and scatter-loss (Lrts) 32

2.7 Relevant value for multiscreen diffraction loss

Lmsd 33

2.8 Values for P ro and γ 36

3.1 Some basic GUI 45

5.1 Path loss model 55

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LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Block diagram of a typical Wireless

Communication System 3

1.2 Frequencies of radio spectrum is classified into

multiple groups 5

1.3 Received power for different value of Loss

parameter v 6

1.4 Mechanisms of propagation model 7

1.5 Reflection mechanisms 8

1.6 Diffraction mechanisms 9

2.1 Basic median path loss relative to free space in

urban areas over quasi-smooth terrain 25

2.2 Base station height/gain factor in urban areas as

a function of range with reference height = 200m 25

2.3 Vehicular antenna height/gain factor in urban

areas as a function of frequency and urbanisation

with reference height = 3m 26

2.4 Method of calculating the effective base station

antenna height 26

2.5 Example path loss predicted by Hata’s model 28

2.6 Average Path Loss for Urban Areas 29

2.7 Parameters used in Walfisch-Ikegami model 31

2.8 Definition of street orientation 31

2.9 Parameter for Lee Model 34

2.10 Rain Loss for ITU Rain Zones at 0.9999

Availability (or 0.01% Un-availability) 38

3.1 Figure Window showing examples of

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MATLAB GUI elements 44

4.1 Flow chart on interface for Hata Model 47

4.2 Hata model Interface 48

4.3 Property Inspector 49

4.4 M-file automatically created by guide after

save the layout area 50

4.5 Warning Box 51

4.6 Interface 52

xiii

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Callbacks Function / Command 59

B Comparisons of various types of model 73

C Glossary of terms 74

CHAPTER 1

INTRODUCTION

1.0 Project Objectives

The objective of this project is related to the study of various prediction

models for mobile radio communication system in order to predict the coverage of

the base station. It also involves literature review of different prediction models

available.

This project will also involve a simulation model based on propagation

prediction model which the simulation will be design on Hata - Okumura Model

using Matlab software.

At the end of this project, complete reports on designing simulations using

Matlab will be produced.

1.1 Problem Statement

Hata Model is the popular model that being used to calculated the losses in

urban, sub-urban and open areas. This model can improve the problems that came

from rough terrain, buildings, reflection, moving vehicle and shadowing which bring

bad accuracy to the radio communication. This model is being extended from

Okumura Model which all of the graphical form is described into mathematical form

in Hata Model. In order to make sure that all of the calculations is easier to know and

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accurate, a design on Interface for Hata Model has to be made. This interface can be

very useful for the user to make calculations without any doubt and easy.

1.2 Thesis Outline

The first chapter will be focus on basic communication where it describe

radio wave propagation , studied the channel and their limitations and some basic

problems such as reflection, scattering, diffraction of signal by natural and human-

made structures which result to attenuation problems.

Chapter two is focus on various types of radio propagation model which will

be covered Indoor and Outdoor Attenuation model. Some overview on Matlab and

GUI software will be covered in chapter three. It will describe on GUI basic tools

that will be used in this simulation.

The result for this project and outcomes is in chapter four which include the

interface development for Hata Model. Lastly, some discussion and summary about

this project is covered in the last chapter.

1.3 Wireless Communication

Communication between the sending and receiving is accomplished by the

propagation of electromagnetic radio waves through the ground and atmosphere. All

communication system operates in the same channel and this will make interference

from every other. This interference can be avoided by implementing geographic or

frequency separation. Below is depicts of typical wireless communication system.

3

Figure 1.1 Block diagram of a typical Wireless Communication System

1.4 Radio Spectrum Classification

The radio spectrum is divided into sub-bands based on each frequency range's

suitability for a given set of applications. Suitability is determined as a function of

the atmospheric propagation characteristics of the given frequencies as well as

system aspects, such as required antenna size and power limitations.

Based on these considerations, the radio spectrum has been divided into the

following sub bands:

a) Extremely Low Frequency (ELF) 300 - 3000 Hz (λ =1000 - 100 km)

Very Low Frequency (VLF) 3 - 30 kHz (λ =100 - 10 km)

Propagation Characteristics: Propagates between the surface of the Earth and

the Ionosphere. Can penetrate deep underground and underwater. As the required

antenna size is proportional to the wavelength, the large wavelength in this case

mandates the use of large antennas.

Applications: mining, underwater communication (submarines), SONAR.

b) Low Frequency (LF) 30 - 300 kHz (λ =10 - 1 km)

Propagation Characteristics: The sky wave can be separated from the ground

wave for frequencies above 100 kHz. This enables communication over large

distances by reflecting the sky wave off the atmosphere.

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Applications: broadcasting, radio navigation.

c) Medium Frequency (MF) 300 - 3000 kHz (λ =1000 - 100 m)

Propagation Characteristics: The sky wave separates from the ground wave in

this range. Ground wave gives usable signal strength up to 100 km from transmitter.

Applications: AM radio broadcasting (550 - 1600 kHz).

d) High Frequency (HF) 3 - 30 MHz (λ =100 - 10 m)

Propagation Characteristics: The sky wave is the main propagation mode.

The ground wave is used for communication over shorter distances than the sky

wave. As propagation loss increases with frequency increases, the use of repeaters is

required.

Applications: Broadcasting over large areas, amateur radio (ham), citizens band (CB)

radio.

e) Very High Frequency (VHF) 30 - 300 MHz (λ =10 - 1 m)

Propagation Characteristics: Diffraction (bending of waves due to

obstruction) and reflection give rise to communication beyond the horizon.

Propagation distances are thousands of kilometers. The diffraction and reflection

enables reception within buildings.

Applications: broadcast TV, FM radio (88 - 108 MHz), radio beacons for air traffic

control.

f) Ultra High Frequency (UHF) 300 - 3000 MHz (λ =1 m - 10 cm)

Propagation Characteristics: Reflections from atmospheric layers are

possible. Effects of rain and moisture are negligible.

Applications: broadcasting, satellite (TV) broadcasting, all (1G to 3G) land mobile

phones, cordless phones, some air traffic control.

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g) Super High Frequency (SHF) 3 - 30 GHz (λ =10 - 1 cm)

Propagation Characteristics: Range becomes limited by obstacles as

frequency increases. Propagation is limited by absorption by rain and clouds.

Applications: Satellite service for telephony and TV, mobile services in the future.

h) Extremely High Frequency (EHF) 30 - 300 GHz (λ =10 - 1 mm)

Propagation Characteristics: Very high losses due to water, oxygen, vapor.

Applications: communications at short distances (within line of sight), broadcast

satellite for HDTV (for communication between satellites in space, not space to

earth).

Figure 1.2 Frequencies of radio spectrum is classified into multiple groups

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1.5 Propagation in free space loss

Propagation in free space is the ideal. Generally the received power can be

expressed as:

For non-Line of sight received power at any distance d can be expressed as:

[ ]

+=

d

dvdPdP

ref

refrr 1010 log10)(log10)(

Figure 1.3 Received power for different value of Loss parameter v

Path Loss formula is expressed as:

(1.1)

(1.2)

(1.3)

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When propagation takes place close to obstacles, the following propagation

mechanisms occur:

Figure 1.4 Mechanisms of propagation model

a) Reflection will occur when a radio wave strikes an object with dimensions

that are large relative to its wavelength, for example buildings. Perfect conductors

will reflect with no attenuation. Dielectrics reflect a fraction of incident energy such

as “Grazing angles” reflect max and steep angles transmit max. (max -The exact

fraction depends on the materials and frequencies involved). The reflection induces

180° phase shift.

When electrical signal propagating through a medium impinges on a different

medium with different electrical characteristics, the electrical signal is partly

reflected back to the previous media and part of the signal is transmitted through the

obstructing medium. If the signal is propagating through a dielectric medium, there is

no absorption of the signal due to reflection. Otherwise part of the energy of the

signal will be absorbed by the medium. If the reflected media is a perfect conductor,

all energy of the signal is reflected back to the first medium.

The intensity of the electric field for the transmitted and reflected signals are

related to the incident electrical signal through the Fresnel Reflection Coefficient

(G). The Fresnel Reflection Coefficient depends on the properties of the material,

like permeability(m), permittivity(e) and conductance(s) of the two media and the

frequency of the propagating wave.

8

Figure 1.5 Reflection mechanisms

b) Diffraction

Diffraction will occurs when a radio wave is obstructed by surfaces with

irregularities. Diffraction allows radio signals to propagate around the curved

surface of the earth and that in turn allows the propagation to travel behind a building

or obstruction. The received signal drops significantly as the receiver moves deep

behind an obstruction. The phenomenon of diffraction is explained by Huygen’s

principle. It states that all signal points on the signal wave acts as a point source to

produce the secondary signal waves that travels in the direction of propagation.

Secondary waves arise from the obstructing surface and give rise to the

bending of waves around and behind obstacles. “Secondary” waves propagated into

the shadowed region. This make the excess path length results in a phase shift.

Fresnel zones relate phase shifts to the positions of obstacles. These secondary

waves reaches the shadowed region of the obstruction and the vector sums of all

these secondary waves provides the signal to the receiver.

The phase difference between the direct line of sight path and the diffracted

path depends upon the height of the obstruction and the locations of the transmitter

and receiver.

θ θr θ

t

9

Figure 1.6 Difraction mechanisms.

c) Scattering

Scattering will occurs when a radio wave travels through a medium

containing lots of small (compared to wavelength) objects.

The actual signal received at a mobile station, is often stronger than the signal

strength estimated by considering reflection and diffraction of signals. The reason for

this is the Scattering. When radio waves hits a rough surface, the reflected energy is

scattered in different directions. Many natural objects like trees and man-made

structures like electrical lamp posts scatter radio energy in all directions. This

scattered signal reaches the receiver and increases the signal strength. The scattering

depends upon the roughness of the surface. Surface Roughness is stated in terms of

the Rayleigh criteria, defined in terms of critical height (hc) of surface protuberances

for given incident angle of reflection(θi)

hc = l / 8SinθI (1.4)

A surface is considered smooth if its minimum to maximum protuberances is

less than hc. and it is considered rough when the minimum to maximum

protuberances is more than hc . On rough surface, the reflected signal energy is

reduced due to this scattering effect. For distant objects, where the physical location

T R

1st Fresnel zone

Obstruction

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of the object is known, Radar Cross Section Model of the object can be used to

predict the scattering effect.

1.6 Summary

Radio waves are a form of electromagnetic radiation, which was discovered

in the late 19th century. The branch of physics that describes how antennas and

radiation behave is called electrodynamics. Many design decisions in layers above

wave propagation are affected by the issues mentioned.

There are several factors have to be taken into account in deciding what

frequency band should be used for a particular type of radio communication service.

Operating frequencies must be chosen in a region of the RF spectrum where it is

possible to design efficient antennas of a size suitable for mounting on base station

masts, vehicles and on hand portable equipment. Since the mobiles can moved

around freely within the area covered by the radio system, their exact location is

unknown and the antennas must therefore radiate energy uniformly in all directions.

Based on the fact that each individual telecommunication link has to

encounter different terrain, path, obstructions, atmospheric conditions and other

phenomena, it is impossible to formulate the exact loss for all telecommunication

systems in a single mathematical equation. As a result, different models exist for

different types of radio links under different conditions. The models rely on

computing the median path loss for a link under a certain probability that the

considered conditions will occur.

Finally, mobile systems must efficiently manage the scarce frequency bands.

Choosing the correct frequency will leads to a better and sufficient outcomes.

CHAPTER 2

RADIO PROPAGATION MODELS

2.1 Introduction

There are two basic types of propagation prediction models which are

empirically based and calculation based.

Empirical models are generally based on the original work of Okumura in the

mid 1960’s. This provides coefficients which are applied to the ideal propagation

figures depending on the nature of the terrain in the propagation path.

Calculation models are making use of the unknown characteristics of objects

in a propagation path. A detailed terrain and clutter database must then be used to

calculate the propagation path loss from the transmitter to the point under

consideration.

A Radio Propagation, is also known as the Radio Wave Propagation Model

is an empirical mathematical formulation for the characterization of radio wave

propagation as a function of frequency, distance and other conditions. A single

model is usually developed to predict the behavior of propagation for all similar links

under similar constraints.

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2.2 Types of Radio Propagation

2.2.1 Indoor Attenuations

2.2.1.1 Physical Effects

Indoor attenuations will have effects from obstacles such as walls, ceilings

and furniture usually block the path between receiver and transmitter. It also depends

on the building construction and layout, the signal usually propagates along corridors

and into other open areas.

Indoor radio propagation is ruled by multiple reflection, diffraction and

scattering from natural and man-made obstructions in the indoor channel. However

the circumstances vary much more than in outdoor environments. The received

signal of an antenna mounted on a desk at an open space office with partitions are

very different from those received at an antenna mounted on the outdoor propagation

links. The small propagation distances make it more difficult to insure far-field

radiation for al the receiver locations and types of antennas. Partitions are amongst

the main indoor signals losses reasons, they occur when terminal antennas are

assembled at the same floor and losses between floors occur when terminals are in

clutter (NLOS) conditions.

The problem of modeling radio wave penetration into buildings differs from

the more familiar vehicular case in several respects. In particular:

a) The problem is truly three-dimensional because at fixed distance from the

base station the mobile can be at a number of heights depending on the floor of the

building where it is located. In an urban environment it may result in there being an

LOS path to the upper floors of many buildings, whereas it is a relatively rare

occurrence in city streets.

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b) The local environment within a building consists of a large number of

obstructions. These are constructed of a variety of materials in close proximity to the

mobile and their nature and number can change over quite short distances.

Indoor radio differs from normal mobile radio in two important respects

which are the interference environment and the fading rate. The interference

environment is often caused by spurious emissions from electronic equipment such

as computers and the level sometimes be much greater than measured outside. It also

have a substantial variations in signal strength from place to place within a building.

The signal can be highly attenuated after propagating a few metres through walls,

ceilings and floors or may still be very strong after propagating several hundred

metres along a corridor. The signal – to – interference ratio is unpredictable and

highly variable.

Unsatisfactory performance in wideband systems can also be caused by

intersymbol interference due to delay spread and limits of data rate. In narrowband

systems, multipath and shadow fading limit the coverage whereas interference causes

major problems even within the intended coverage area.

Table 2. 1 Measure of accuracy of simple model.

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2.2.1.2 Examples of Indoor Models

a) ITU Indoor Propagation Model

The ITU Indoor Propagation Model is used to estimates the path loss inside a

room or a closed area inside a building delimited by walls of any form. It is suitable

for appliances designed for indoor use which it approximates the total path loss an

indoor link may experienced. This model is applicable to only indoor environments.

Typically such appliances use lower microwave bands around 2.4 GHz. The

coverage is 900 MHz to 5.2 GHz. The formula that being used is:

L = 20 log f + N log d + Pf (n) – 28 (2.1)

Where;

L = the total path loss. Unit: decibel (dB).

f = Frequency of transmission. Unit: megahertz (MHz).

d = Distance. Unit: meter (m).

N = The distance power loss coefficient.

n = Number of floors between the transmitter and receiver.

Pf(n) = the floor loss penetration factor.

The distance of power loss coefficient, N is the quantity that expresses the

loss of signal power with distance. This coefficient is an empirical and some of the

values are provided as below:

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Table 2.2 Loss of signal power with distance

Frequency Band Residential Area Office Area Commercial Area

900 MHz N/A 33 20

1.2 GHz N/A 32 22

1.3 GHz N/A 32 22

1.8 GHz 28 30 22

4 GHz N/A 28 22

5.2 GHz N/A 31 N/A

The floor penetration loss factor is an empirical constant dependent on the

number of floors the waves need to penetrate. Some of the values are tabulated in

Table 2.3:

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Table 2.3 Floor penetration loss factor.

[Frequency

Band

Number of

Floors

Residential

Area

Office

Area

Commercial

Area

900 MHz 1 N/A 9 N/A

900 MHz 2 N/A 19 N/A

900 MHz 3 N/A 24 N/A

1.8 GHz n 4n 15+4(n-1) 6 + 3(n-1)

2.0 GHz n 4n 15+4(n-1) 6 + 3(n-1)

5.2 GHz 1 N/A 16 N/A

b) Log Distance Path Loss Model

This model predicts path loss a signal encounters inside a building over

distance. This model is applicable to indoor propagation modeling.

Log Distance Path Loss model is formally expressed as:

L = Lo + 10 γ log10 do

d+ Xg (2.2)

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Where;

L = The total path loss inside a building. Unit: Decibel (dB)

L0 = The path loss at reference distance, usually, 1 km or 1 mile. Unit: Decibel (dB)

γ = The path loss distance exponent.

Xg = A Gaussian random variable with zero mean and standard deviation, reflecting

the shadow fading or slow fading.

The calculation of empirical coefficients is shown in the table below:

Table 2.4 Calculations of coefficients 10γ and σ in dB

Building Type Frequency of Transmission 10γ σ

Retail store 914 MHz 22 8.7

Grocery store 914 MHz 18 5.2

Office with hard paritition 1.5 GHz 30 7

Office with soft partition 900 MHz 24 9.6

Office with soft partition 1.9 GHz 26 14.1

Textile or chemical 1.3 GHz 20 3.0

Textile or chemical 4 GHz 21 7.0, 9.7

Metalworking 1.3 GHz 16 5.8

Metalworking 1.3 GHz 33 6.8

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2.2.2 Outdoor Attenuations

Outdoor propagation models are used to understand the link performance of

Macro Cellular systems. The propagation of radio waves is strongly influenced by

the nature of the environment, the size and buildings. A qualitative description of the

environment is often used a term such as rural, urban, suburban and open areas. The

term rural defines open farmland with sparse buildings, woodland and forests. These

qualitative descriptions are open to different interpretations by different users based

on measurements made in one city are generally applicable elsewhere.

Examples of Outdoor Attenuations are stated as below:

2.2.2.1 Foliage Models

a) Weissberger’s Modified Exponential Decay Model

Weissberger’s Modified Exponential Decay Model, or simply, Weissberger’s

Model, is a radio wave propagation model that estimates the path loss due to the

presence of one or more trees in a point-to-point telecommunication link. This model

belongs to the category Foliage or Vegetation models. It is formulated in 1982 being

develop of ITU Model for Exponential Decay.

This model is applicable to the cases of line of sight propagation. For

example is microwave transmission. This model only applicable when there is an

obstruction made by some foliage in the link between the transmitter and receiver. It

is ideal in the situation where the LOS path is blocked by dense, dry and leafy trees.

The frequency for this model is 230 MHz to 95 GHz and the depth of foliage is up to

400 m.

This model is only significant for frequency range 230 MHz to 95 GHz as

pointed by Blaunstein. The limitations for this model are it does not defines the

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operation if the depth of vegetations is more than 400m. Weissberger’s is formally

formulated as:

L = 1.33 f 0.284

d 0.588

, if 14 < d ≤ 400

0.45 f 0.284

d , if 0 < d ≤ 14 (2.3)

Where

L = The loss due to foliage. Unit: decibels (dB)

f = The transmission frequency. Unit: gigahertz (GHz)

d = The depth of foliage ‘’’along’’’ the path. Unit: meters (m)

b) Early ITU Model

The ITU Vegetation Model is a radio propagation model that estimates the

path loss encountered due to the presence of one or more trees inside a point to point

telecommunication link. The predictions found from this model is congruent to those

found from Weissberger’s Modified Exponential Decay Model in low frequencies.

This model is adopted in late 1986 from the CCIR predecessor of ITU.

This model is applicable on the situations where the telecommunication link

has some obstructions made by trees along its way. It also suitable for point-to-point

microwave links that has a vegetation in their path. The typical application of this

model is to predict the path loss for microwave links.

The limitation of this model is the result of this model will be impractical at

high frequencies. The model is formulated as:

L = 0.2 f 0.3

d 0.6

(2.4)

Where;

L = The path loss. Unit: decibel (dB)

f = The frequency of transmission. Unit: megahertz (MHz)

d = The depth of foliage along the link: Unit: meter (m)

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2.2.2.2 Terrain Models

a) Egli Model

Egli Model is a terrain model for radio frequency propagation. This model

predicts the total path loss for a point-to-point link. Typically used for Line of Sight

transmission, this model provides the path loss as a single quantity.

This model is suitable for cellular communication scenarios where one

antenna is fixed and other is mobile. It is applicable to the scenario where the

transmission has to go over an irregular terrain. Egli model is not applicable to a

scenario where some vegetative obstruction is in the middle of the link. This model

predicts the path loss as a whole and does not subdivide the loss into space loss and

other losses.

Egli model is formally expressed as:

L = GBGM

22

2

40

fd

hbhm (2.5)

Where;

GB = Gain of the base station antenna. Unit: dimensionless

GM = Gain of the mobile station antenna. Unit: dimensionless

hB = Height of the base station antenna. Unit: meter (m)

hM = Height of the mobile station antenna. Unit: meter (m)

d = Distance from base station antenna. Unit: meter (m)

f = Frequency of transmission. Unit: megahertz (MHz)

b) Longley-Rice Model

The Longley-Rice (LR) radio propagation model is a method for predicting

median path loss for a telecommunication link in the frequency range of 20 MHz to

20 GHz. LR is also known as Irregular Terrain Model (ITM). It was created for the

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needs of frequency planning in TV broadcasting in USA in 1960s and was

extensively used for preparing the tables of channel allocations for VHF/UHF public

broadcasting in USA. LR has two parts which are a model for predictions over an

area and a model for point-to-point link predictions.

The method may be used either with detailed terrain profiles for actual paths

or with profiles representatives of median terrain characteristics for a given area. It

includes estimates of variability with time and location and a method of computing

service probability. The range for this model is 1 to 2000 km and antenna heights are

from 0.5 to 3000 m. The formulation can be expressed as:

L = d

−

−

12

21

dd

AA + 5 log 10 [ ]

∆−+ −54

1

10)(5.0)(78.01 xdhdhfhh cbm ℓ (2.6)

Where:

A1 and A2 is diffraction losses

∆h is as stated in table 2.4 below:

Table 2.5 Estimated values of ∆h

Type of terrain ∆h

Water or very smooth plains 0 – 5

Plains ~ 30

Hills 80 – 150

Mountains 150 – 300

Rugged Mountains 300 - 700

c) ITU Terrain Model

The ITU Terrain Loss Model is a radio propagation model that provides a

method to predict the median path loss for a telecommunication link. Developed on

the basis of diffraction theory, this model predicts the path loss as a function of the

height of path blockage and the First Fresnel zone for the transmission link.

22

This model is applicable on any terrain. This model accounts for obstructions

in the middle of the telecommunications link, and is suitable to be used inside cities

as well as in open fields. It is ideal for modeling a Line of sight link in any terrain.

This model is considered valid for losses over 15 dB.

The model is mathematically formulated as:

A = 10 – 20 ( hL – h0 ) ( 17.3 fd

dd 21 ) (2.7)

CN = h F1

Where:

A = Empirical Diffraction Loss. Unit: Decibel(dB)

CN = Normalized terrain clearance. Unit: None.

h = The height difference. Unit: Meter (m)

hL = Height of the line of sight link. Unit: Meter(m)

h0 = Height of the obstruction. Unit: Meter(m)

F1= Height of First Fresnel Zone. Unit: Meter(m)

d1= Distance of obstruction from one terminal. Unit: Meter(m)

d2= Distance of obstruction from the other terminal. Unit: Meter(m)

f = Frequency of transmission. Unit: Megahertz(m)

d = Distance from transmitter to receiver. Unit: Meter (m)

2.2.2.3 City Models

a) Young Model

Young model is a Radio propagation model that was builds on the data

collected on New York City. It typically models the behaviour of cellular

communication systems in large cities. It was built on the data at New York City in

23

1952. This model is ideal for modelling the behaviour of the cellular communications

in large cities with tall structures. The coverage for this model is 150 MHz to 3700

MHz.

The mathematical formulation for Young Model is:

L = GBGM β2

2

d

hh MB (2.8)

Where:

L = The Path loss. Unit: Decibel (dB)

GB = Gain of Base transmitter. Unit: Decibel (dB)

GM = Gain of Mobile transmitter. Unit: Decibel (dB)

hB = Height of Base station antenna. Unit: Meter (m)

hM = Height of Mobile station antenna. Unit: Meter (m)

d = Link distance. Unit: Kilo Meter (km)

β = Clutter factor

b) Okumura Model

This is the most popular model that being used widely The Okumura model

for Urban Areas is a Radio propagation model that was built using the data collected

in the city of Tokyo, Japan. The model is ideal for using in cities with many urban

structures but not many tall blocking structures. The model served as a base for Hata

models. Okumura model was built into three modes which are urban, suburban and

open areas. The model for urban areas was built first and used as the base for others.

Clutter and terrain categories for open areas are there are no tall trees or

buildings in path, plot of land cleared for 200 – 400m. For examples at farmland, rice

fields and open fields. For suburban area the categories is village or highway

scattered with trees and houses, few obstacles near the mobile. Urban area categories

is built up city or large town with large buildings and houses with two or more storey

or larger villager with close houses and tall, thickly grown trees.

24

Formula for Okumura Model is expressed below:

Lm(dB) = LF(d)+ Amu(f,d) – G(hM) – G(hB) – GAREA (2.9)

Where;

Lm = (i.e., median) of path loss

LF(d) = free space propagation pathloss.

Amu(f,d) = median attenuation relative to free space

G(hB) = base station antenna heigh gain factor

G(hM) = mobile antenna height gain factor

G(hB) = 20log(hB/200) 1000m > hB > 30m

G(hM) = 10log(hM/3) hM<= 3m

G(hM) = 20log(hM/3) 10m > hM > 3m

GAREA: gain due to type of environment given in suburban, urban or open areas

Correction factors like terrain related parameters can be added using a

graphical form to allow for street orientation as well as transmission in suburban and

open areas and over irregular terrain. Irregular terrain is divided into rolling hilly

terrain, isolated mountain, general sloping terrain and mixed land-sea path. The

terrain related parameters that must be evaluated to determine the various corrections

factors areas shown in the figure below:

25

Figure 2.1 Basic median path loss relative to free space in urban areas over

quasi-smooth terrain.

Figure 2.2 Base station height/gain factor in urban areas as a function of range

with reference height = 200m.

26

Figure 2.3 Vehicular antenna height/gain factor in urban areas as a function of

frequency and urbanisation with reference height = 3m.

Figure 2.4 Method of calculating the effective base station antenna height.

Where:

Hb (Effective base station antenna height) = the height of the base station antenna

above the average ground level calculated over the range interval 3-15 km or less in a

direction towards the receiver.

27

This Okumura model probably remains the most widely quoted of he

available models. It has come to be used as a standard by which to compare others

since it is intended for use over a wide variety of radio paths encompassing not only

urban areas but also different types of terrain.

c) Hata Model

Hata established empirical mathematical relationships to describe the

graphical information given by Okumura. Hata’s formulation is limited to certain

ranges of input parameters and is applicable only over quasi-smooth terrain. The

mathematical expression and their ranges of applicability are as follows:

Carrier Frequency: 150 MHz ≤ fc ≤1500 MHz

Base Station (BS) Antenna Height: 30 m ≤hb ≤200 m

Mobile Station (MS) Antenna Height: 1 m ≤hm ≤10 m

Transmission Distance: 1 km ≤d ≤20 km

(2.10)

Where:

(2.11)

28

(2.12)

The path loss predicted by Hata's model, example values (hb =70 m, hm=1.5

m, fc=900 MHz) is depicted in figure below:

Figure 2.5 Example path loss predicted by Hata’s model

Below is figure for path loss in urban areas for Carrier frequency = 900 MHz, hb =

150m and hm = 1.5m

29

Figure 2.6 Average Path Loss for Urban Areas

These expressions have considerably enhanced the practical value of the

Okumura method, although Hata’s formulations do not include any of the path

specific corrections available in the original model.

d) Cost 231 Model

Most future PCS systems are expected to operate in the 1800-2000 MHz

frequency band. It has been shown that path loss can be more dramatic at these

frequencies than those in the 900 MHz range. Some studies have suggested that the

path loss experienced at 1845 MHz is approximately 10 dB larger than those

experienced at 955 MHz, all other parameters being kept constant.

30

The COST231-Hata model extends Hata's model for use in the 1500-2000

MHz frequency range, where it is known to underestimate path loss. The model is

expressed in terms of the following parameters:

Carrier Frequency fc 1500-2000 MHz

BS Antenna Height hb 30-200 m

MS Antenna Height hm 1-10 m

Transmission Distance d 1-20 km

The path loss according to the COST231-Hata model is expressed as:

(2.13)

Where;

(2.14)

While both the Hata and COST231 are designed for use with base station

antenna heights greater than 30 meters, they may be used with shorter antennas

provided that surrounding buildings are well below this height. Neither model should

be used to predict path loss in an urban canyon. Lastly, the model should not be used

for prediction with transmission distances below 1 km, as path losses become highly

dependent on local topography below this range.

e) Cost 231 Walfisch-Ikegami Model

This model is proposed of a combination of the Walfisch-Bertoni method and

Ikegami model to improve path loss estimation through the inclusion of more data.

Four factors are included which are heights of building, width of roads, building

31

separation and road orientation with respect to the LOS path. This model is restricted

to the following range of parameters:

fc = 800 to 2000 MHz

hb = 4 to 50 m

hm = 1 to 3 m

d = 0.02 to 5 km

Figure 2.7 Parameters used in Walfisch-Ikegami model

Figure 2.8 Definition of street orientation

32

This model distinguishes between LOS and non-LOS paths as follows. For

LOS paths the equation is as below:

L = 42.6 + 26log d + 20log f for d ≥ 0.020 km (2.15)

Where :

L = L0 + Lrts + Lmsd

L =32.4 +20log d+ 20log f

Lrts = rooftop-to-street diffraction and scatter-loss.

Lrts = -16.9 -10 log w+10log f + 20 log (hb - hr ) + Lori for hb > hr

Lmsd = the multiscreen diffraction loss.

Lmsd= Lbsh+ ka+ kd log d+ k f log f - 9 log b

Table 2.6 Relevant value for rooftop-to-street diffraction and scatter-loss (Lrts)

33

Table 2.7 Relevant value for multiscreen diffraction loss Lmsd:

If the structure of buildings and streets are unknown, the following values are

recommended :

b =20 to 50 m

w=b/2

hr=3{number of floors}+roof (roof=3 for pitched, roof=0 for flat)

φ=90o

This model gives predictions which agree quite well with measurements

when the base station antenna is above rooftop height, producing mean errors of

about 3db with standard deviations in the range 4-8 db. However the performance

deteriorates as hb approaches hr and is quite poor when hb<<hr. The model produces

large errors in the microcellular situation.

f) Lee Model

Named after W.C.Y. Lee, this empirically derived path loss model is

parameterized by ro P , the power at the 1-mile point of interception, and γ, an

experimentally determined path loss slope. It is indicated for use with flat terrain and

is specified as follows:

34

Figure 2.9 Parameter for Lee Model

(2.16)

Where:

Pr = field strength of the received signal at a distance r from the transmitter

P ro = received power at 1 mile (1.6 km)

r = distance between MS and BS antennas

r0 = 1 mile (1.6 km)

= path loss slope (experimentally determined)

f = actual carrier frequency

f0 = nominal carrier frequency, (= 900 MHz)

n = empirically derived exponent. depends on geographical locations and operating

frequency ranges. 2 ≤n ≤3.

n=2 is recommended for a suburban or open area with f < 450 MHz. Use n=3 for an

urban area with f > 450 MHz.

α0 = correction factor—accounts for antenna heights, transmit power and antenna

gains which differ from nominal values.

The limitations for this model is:

Carrier frequency = 900 MHz

Base Station antenna height = 30.48m

ro = 1mil

= 1,6

km

r Pr

P

35

Bs transmit power = 10 watts

Bs antenna gain = 6db above dipole gain

Mobile station antenna height = 3m

Mobile station Gain = 0 db above dipole gain

Correction Factor (α0):

Note that the actual frequency of the transmitted signal does not explicitly appear in

the formulae specifying α0. The formula is a general one which is valid for all

frequencies greater than 30 MHz.

(2.17)

The exponent v, which appears in the expression above for α2 is also derived

from empirical data and is specified as:

(2.18)

36

The following table lists some example values for the empirically derived

quantities P ro and γ:

Table 2.8 Values for P ro and γ

The path loss formula is express as below:

(2.19)

Where :

Pt is the transmit power.

The data in Table 2.8 is used to write the path los expressions for the various

environment as stated below:

37

(2.19)

2.2.3 Environmental Effects

There are differences between the two most popular models, ITU terrestrial

model and Crane model. Crane has produced three models; the Crane Global model,

Crane two-component model, and revised Crane two-component model that produce

slightly different estimates of the long term mean fade probability. The Crane models

tend to produce higher attenuation than the ITU model. But the uncertainty of either

of these models or alternatively the short-term expectation of fade is quite large.

Uncertainty stems from variations from year-to-year and location-to-location.

a) ITU Rain Attenuation model

This is also one example of environmental effects. Rain attenuation is a major

constraint in microwave radio link design above 10 GHz. Several empirical and non-

empirical rain attenuation prediction models that have been developed are based on

the measured data obtained from temperate regions. Most of these existing rain

attenuation prediction models do not appear to perform well in high rainfall regions.

cumulative distribution empirical evidence shows that the ITU-R model

underestimates the measured rain attenuation cumulative distribution when applied to

38

tropical regions, leading to a poor prediction. Other impairments are due to gaseous

absorption, cloud, troposphere refractive effects, scintillation, wet antenna etc.

The specific attenuation, γ is a function of the rainfall rate, R0.01exceeded at

0.01% of time is given by:

αγ )( 01.0Rk= dB/km (2.20)

where:

k and α are frequency and polarization parameters, given by ITU-R recommendation

Figure 2.10 Rain Loss for ITU Rain Zones at 0.9999 Availability (or 0.01% Un-

availability)

39

Rain loss for ITU rain zones is shown in Figure 2.10 for path lengths up to 5

km and for an availability of 0.9999. Capabilities are shown in dotted lines for an

example QPSK system with EIRP density values of the US FCC EIRP limit,

Canadian limit, and a typical system having 0 dBW/MHz EIRP. An EIRP density of

0 dBW/MHz is within today’s technology and provides fade margin from 20 – 30

dB. With a subscriber antenna gain of 35 dBi, this would support links up to 5 km in

some rain zones.

b) Crane model

Crane determined that the distribution of deviations was lognormal and

presents a model for variability in terms of risk. Standard deviation of the natural

logarithm of rain-rate, Sm, was obtained as follows:

Year-to-year, Sm = 0.21

Location-to-location, Sm = 0.17

Combined year-to-year and location-to-location, Sm = 0.28

The year-to-year standard deviation corresponds to 23% in dB and the

combined standard deviation corresponds to 32% in dB. A risk model is presented by

Crane to estimate the attenuation for any year over a selected number of years using

the variability standard deviation.

2.3 Summary

Several predictions method has been described in his chapter. They all aim to

predict the median signal strength either at a specified receiving point or in a small

area. Receiving point methods are needed for point-to-point links whereas small area

methods are useful for base-to-mobile paths where the precise location of the

receiver is not known. All of these methods have been available for many years and

have stood the test possibly with modification and updating. They differ widely in

40

approach, complexity and accuracy. But sometimes, when it comes to accuracy, no

one method outperforms all others in all conditions.

Statistical methods are based on measured and average losses along typical

classes of radio links. Among the most commonly used such methods are COST 231,

Okumura-Hata, Lee model and others.

Deterministic methods based on the physical laws of wave propagation are

also used Ray Tracing is such one method. These methods are expected to produce

more accurate and reliable predictions of the path loss than the empirical methods.

However they are significantly more expensive in computational effort and depend

on the detailed and accurate description of all objects in the propagation space such

as buildings, roofs, windows, doors and walls. For these reasons they are used

predominantly for short propagation paths.

Every propagation models has its own advantage and disadvantage. Choosing

a method appropriate to the specific problem under consideration is a vital step in

reaching a valid prediction.

CHAPTER 3

SIMULATION USING MATLAB

3.1 Overview of Matlab

Matlab is a high performance language for technical computing. It integrates

computation, visualization and programming in an easy to use environment where

problems and solutions are expressed in familiar mathematical notation.

Matlab stands for matrix laboratory. It is an interactive system whose basic

data element is an array that does not require dimensioning that will allows us to

solve many technical computing problems especially those with matrix formulas, in a

fraction of time it would take to write a program in a scalar non-interactive language

as C or FORTRAN.

3.2 Why do I choose Matlab Software?

Matlab has many advantages compared to conventional computer language

for technical problem solving. Among them are:

a) Easy to use

Matlab has an interpreted language an its very easy to use. The program can

be used as a scratch to evaluate expressions type at the command line or it can be

42

used to execute large pre-written programs. Because the language is so easy to use, it

is ideal for educational use and for the rapid prototyping of new programs.

b) Platform independence

Matlab is supported on many different computer systems, provide large

measure of platform independence. Programs written on any platform will run on all

of the other platform and data files written on any platform can be read transparently

on any other platform. As a result, program written in Matlab can migrate to new

platform when the needs of the user change.

c) Graphical User Interface

Matlab also include tools that allow a programmer to interactively construct a

graphical user interface (GUI) for her program. With this capability, the programmer

can design sophisticated data analysis programs that can be operated by relatively

inexperienced users.

3.3 Graphical User Interface (GUI)

A graphical user interface (GUI) is a pictorial interface to a program. A good

GUI can make programs easier to use by providing them with a consistent

appearance and with intuitive controls like pushbuttons, list boxes, sliders, menus,

and so forth. The GUI should behave in an understandable and predictable manner,

so that a user knows what to expect when he or she performs an action.

For example, when a mouse click occurs on a pushbutton, the GUI should

initiate the action described on the label of the button. This chapter introduces the

basic elements of the MATLAB GUIs.

43

3.4 GUI works

A graphical user interface provides the user with a familiar environment in

which to work. This environment contains pushbuttons, toggle buttons, lists, menus,

text boxes, and so forth, all of which are already familiar to the user, so that he or she

can concentrate on using the application rather than on the mechanics involved in

doing things.

However, GUIs are harder for the programmer because a GUI-based program

must be prepared for mouse clicks (or possibly keyboard input) for any GUI element

at any time. Such inputs are known as events, and a program that responds to events

is said to be event driven. The three principal elements required to create a MATLAB

Graphical User Interface are :

3.4.1) Components

Each item on a MATLAB GUI (pushbuttons, labels, edit boxes, etc.) is a

graphical component. The types of components include graphical controls

(pushbuttons, edit boxes, lists, sliders, etc.), static elements (frames and text strings),

menus, and axes. Graphical controls and static elements are created by the function

uicontrol, and menus are created by the functions uimenu and uicontext menu. Axes,

which are used to display graphical data, are created by the function axes.

3.4.2) Figures.

The components of a GUI must be arranged within a figure, which is a

window on the computer screen. Empty figures can be created with the function

figure and can be used to hold any combination of components.

44

3.4.3) Callbacks

Clicks a mouse on a button or types information on a keyboard. A mouse

click or a key press is an event, and the MATLAB program must respond to each

event if the program is to perform its function. For example, if a user clicks on a

button, that event must cause the MATLAB code that implements the function of the

button to be executed. The code executed in response to an event is known as a call

back. There must be a callback to implement the function of each graphical

component on the GUI. The basic GUI elements are summarized in Table 3.1, and

sample elements are shown in Figure 3.1

Figure 3.1 A Figure Window showing examples of MA TLAB GUI elements.

From top to bottom and left to right, the elements are: (1) a pushbutton; (2) a toggle

button in the 'on' state; (3) two radio buttons surrounded by a frame; (4) a check box;

(5) a text field and an edit box; (6) a slider; (7) a set of axes; and (8) a list box.

45

Table 3.1 Some basic GUI

46

3.5 Creating and displaying GUI

a) Choosing what tools that been used to create Interface

b) Use a MATLAB tool called guide (GUI Development Environment) to layout

the components on a figure. The size of the figure and the alignment and spacing of

components on the figure can be adjusted using the tools built into guide.

c) Use a MATLAB tool called the Property Inspector (built into guide) to give

each component a name (a "tag") and to set the characteristics of each component,

such as its color, the text it displays, and so on.

d) Write code to implement the behavior associated with each callback function.

3.6 Summary

Above is some basic tools and guide on how to use the GUI tools. All of this

basic information is very useful in designing the interface. By using all of the

instruction above, we can easily create the interface.

CHAPTER 4

INTERFACE FOR HATA MODEL

4.1 Flow chart on how to make interface

Draw layout using

Matlab tool

Write code to

implement behavior

each callback

Ok? Yes No

Property Inspector to

characterize each

component

Save figure

to m-file

Done

Start

Sketch layout

to design

48

Figure 4.1 Flow chart on interface for Hata Model

4.2 Layout for Hata Model

Figure 4.2 Hata model Interface

Sketch the GUI and design the layout at the guide tool window. To create the

design, is by dragging the all the chosen button in to the layout window.

49

4.3 Set the properties of the button

Figure 4.3 Property Inspector

In this property Inspector, we can set many properties such as color, size,

font, text, alignment and others. We must set two properties which is the String

property that contains the text to be displayed and the Tag property which is the

name of the pushbutton. This two button is important to locate and update the text

field.

50

4.4 M-file

Figure 4.4 M-file automatically created by guide after save the layout area.

51

The M-file contains code that loads the figure file and creates the GUI with a

callback function for each active GUI component. Each callback function handles

event from a single GUI component. If a mouse click occurs on the GUI component,

then the component’s callback function will be automatically called by Matlab.The

name of the Callback function will be the value in the Tag property of the GUI

component plus the character “Callback”.

4.5 Error Dialog Box

Figure 4.5 Warning Box

This figure shows that if the user gives the wrong values or invalid input, this

dialog error box will appear. A dialog box is a special type of figure that is used to

display information or to get input from the user. Dialog box are used to display

52

errors, provide warnings, answer question or get user input. A dialog box does not

allow any other window in the application to be access until it is dismissed.

4.6 Interfaces for Hata Model

Figure 4.6 Interface

If all the function is called without arguments then the function displays the

GUI contained in proper file with its layout. If there is an arguments, its must be an

error in the m-file callback functions.

CHAPTER 5

DISCUSSION

5.1 Conclusion and recommendations

The prediction of path loss is very important step in planning a mobile radio

system. An accurate predictions method is needed to determine the parameters of

radio base stations which will provide efficient and reliable coverage of specified

areas. None of the model stands out as being ideally suited to all environments. As a

result, choosing an appropriate method to the specific problem under consideration is

a vital step in reaching a valid prediction.

Although there appears to be a huge potential for improved predictions

methods based on deterministic process through the availability of improved

databases of various kinds and the ready availability of small, powerful computers,

the fact remain that currently for macro cells, the hata-Okumura model is still the

most used. This is undoubtedly due to its simplicity and its proven reliability.

However, many variations of the original approach have been proposed where the

basic loss has been combine with further losses calculated using various knife-edge

diffraction model. Alternative methods of defining parameters such as the effective

base station antenna height and establishing the correction factor in irregular terrain

have also been investigated.

It is clear that accurate ways of representing geographical data and efficient

methods of extracting information from the database are essential for the

development of improved and computationally efficient propagation tools.

54

Nevertheless, the strength of the chain is that its weakness link and nowhere

is that more obvious than here. There is little point in having highly accurate methods

for calculating diffraction loss if is depend on obstacles shape still only coarsely

defined. A strategic, integrated approach is surely needed.

In this project, finally the developing of the software on Hata prediction

model has been used to determine the accurate value of the path loss in urban,

suburban and open areas.

Hata model is used as an experiment to make GUI Interface for formal and

informal user. Hata model is used for this experiment because this method is easy to

apply and it is established in mathematical relationship to describe the graphical

information given by Okumura model.

Hopefully by using this interface the calculation on Hata Model will be easy,

simple and accurate to use. This interface is a friendly user where the user only have

to input all the data and push the calculate button to calculate total path loss.

For future works, this interface can be used or implement to other predictions

model by replacing the formulas and layout design according to the chosen

prediction model.

55

5.2 Summary on some propagation model

Table 5.1 Path loss model

In general, the models described are a mixture of empiricism and the

application of the propagation theory. The empirical approach relies on fitting curves

or analytical expressions to sets of measured data and has the advantage of implicitly

taking into account all factors.

The prediction method gives only the median value of the path loss and do

not deal with the subject of variability either implicitly or explicitly. In practice

however, a quantitative measure of signal variability is essential. It should be

claimed that an estimate of the variability is no less important than a prediction of the

median signal strength itself.

56

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E79-B, no. 9, 1192.1198. http://www.ee.uts.edu.au/~hajime/

17. Neskovic, Aleksandar, Neskovic, Natasa, and Paunovic, George. (2000). .

Modern Approaches in Modeling of Mobile Radio Systems

58

Propagation Environment.. IEEE Communications Surveys & Tutorials,

http://www.comsoc.org/pubs/surveys/3q00issue/neskovic.html

18. Nilsson, Martin, Slettenmark, Jesper, and Beckman Claes. (1998). .Wave

Propagation in Curved Road Tunnels.. IEEE AP-S International

Symposium. http://rf.rfglobalnet.com/library/Papers/files/7/apstunnels.pdf

19. Orange, Matthew. (March 1998). .Propagation in Outdoor Cellular and In-

Building Pico- Cellular Systems.. Packetised Wireless Communication

Systems in Interference Limited Environments, 35.50.

http://www.ele.auckland.ac.nz/students/orange/thesis/toc_final.pdf

20. Rapport, Theodore S. (1998). Wireless Communications: Principles &

Practices, Prentice Hall PTR, Saddle River, New Jersey, 140.141.

21. R. Vaughan, J. Bach Andersen (2003), Channels, Propagation and

Antennas for Mobile Communications, IEE, 753 p.

22. Saunders, Simon. (1999 & 2000). Antennas and Propagation for Wireless

Communication Systems. Chichester, West Sussex, England: John Wiley &

Sons Ltd.

23. SSS Online. (January 2001). .Introduction to Indoor Radio Propagation..

Spread Spectrum Scene, 1.6. http://sss-mag.com/indoor.html

24. Thompson, Richard. (2000). .Introduction to HF Radio Propagation.. IPS

Radio & Space Services, 1.28.

http://www.ips.gov.au/papers/richard/hfreport/webrep.html

25. Tripathi, Nishith, Reed, Jeffrey, and Van Landingham, Hugh. (December

1998). .Handoff in Cellular Systems.. IEEE Personal Communications,

26.36. http://ntrg.cs.tcd.ie/htewari/papers/tripathi98.pdf

26. W. Backman (2003), Error Correction on Predicted Signal levels in Mobile

Communications, master thesis.

27. Zeus Wireless. (1999, 2000). .Wireless Data Telemetry.. Zeus Whitepaper

Series, 6.9.http://www.zeuswireless.com/html/about/wirelessconn.html

59

APPENDICES

APPENDIX A – CALLBACKS FUNCTION / COMMAND

function varargout = tryprojek4(varargin)

% TRYPROJEK4 M-file for tryprojek4.fig

% TRYPROJEK4, by itself, creates a new TRYPROJEK4 or raises the existing

% singleton*.

%

% H = TRYPROJEK4 returns the handle to a new TRYPROJEK4 or the handle to

% the existing singleton*.

%

% TRYPROJEK4('CALLBACK',hObject,eventData,handles,...) calls the local

% function named CALLBACK in TRYPROJEK4.M with the given input

arguments.

%

% TRYPROJEK4('Property','Value',...) creates a new TRYPROJEK4 or raises the

% existing singleton*. Starting from the left, property value pairs are

% applied to the GUI before tryprojek4_OpeningFunction gets called. An

% unrecognized property name or invalid value makes property application

% stop. All inputs are passed to tryprojek4_OpeningFcn via varargin.

%

% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one

% instance to run (singleton)".

%

% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help tryprojek4

% Last Modified by GUIDE v2.5 16-Feb-2008 15:44:12

% Begin initialization code - DO NOT EDIT

60

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @tryprojek4_OpeningFcn, ...

'gui_OutputFcn', @tryprojek4_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin & isstr(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before tryprojek4 is made visible.

function tryprojek4_OpeningFcn(hObject, eventdata, handles, varargin)

% This function has no output args, see OutputFcn.

% hObject handle to figure

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

% varargin command line arguments to tryprojek4 (see VARARGIN)

% Choose default command line output for tryprojek4

handles.output = hObject;

% Update handles structure

guidata(hObject, handles);

if strcmp(get(hObject,'Visible'),'off')

61

initialize_gui(hObject, handles);

end

% UIWAIT makes tryprojek4 wait for user response (see UIRESUME)

% uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.

function varargout = tryprojek4_OutputFcn(hObject, eventdata, handles)

% varargout cell array for returning output args (see VARARGOUT);

% hObject handle to figure



% Get default command line output from handles structure

varargout{1} = handles.output;

% --- Executes during object creation, after setting all properties.

function freq_CreateFcn(hObject, eventdata, handles)

% hObject handle to freq (see GCBO)


% handles empty - handles not created until after all CreateFcns called

% Hint: edit controls usually have a white background, change

% 'usewhitebg' to 0 to use default. See ISPC and COMPUTER.

usewhitebg = 1;

if usewhitebg

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));

end

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function freq_Callback(hObject, eventdata, handles)

% hObject handle to freq (see GCBO)



% Hints: get(hObject,'String') returns contents of freq as text

% str2double(get(hObject,'String')) returns contents of freq as a double

freq = str2double(get(hObject, 'String'));

if freq < 100 | freq > 1500

set(hObject, 'String', 0);

errordlg('Invalid input values!','Error Dialog Box');

end

freq = str2double(get(hObject, 'String'));

if isnan(freq)


errordlg('Input must be a number!','Error Dialog Box');

end

data = getappdata(gcbf, 'metricdata');

data.freq = freq;

setappdata(gcbf, 'metricdata', data);


function dist_CreateFcn(hObject, eventdata, handles)

% hObject handle to dist (see GCBO)





usewhitebg = 1;

if usewhitebg

63


else


end

function dist_Callback(hObject, eventdata, handles)

% hObject handle to dist (see GCBO)



% Hints: get(hObject,'String') returns contents of dist as text

% str2double(get(hObject,'String')) returns contents of dist as a double

dist = str2double(get(hObject, 'String'));

if dist < 1 | dist > 20



end

dist = str2double(get(hObject, 'String'));

if isnan(dist)


errordlg('Input must be a number','Error');

end


data.dist = dist;



function hb_CreateFcn(hObject, eventdata, handles)

% hObject handle to hb (see GCBO)



64



usewhitebg = 1;

if usewhitebg


else


end

function hb_Callback(hObject, eventdata, handles)

% hObject handle to hb (see GCBO)



% Hints: get(hObject,'String') returns contents of hb as text

% str2double(get(hObject,'String')) returns contents of hb as a double

hb = str2double(get(hObject, 'String'));

if hb < 30 | hb > 200



end

hb = str2double(get(hObject, 'String'));

if isnan(hb)



end


data.hb = hb;


65


function hm_CreateFcn(hObject, eventdata, handles)

% hObject handle to hm (see GCBO)





usewhitebg = 1;

if usewhitebg


else


end

function hm_Callback(hObject, eventdata, handles)

% hObject handle to hm (see GCBO)



% Hints: get(hObject,'String') returns contents of hm as text

% str2double(get(hObject,'String')) returns contents of hm as a double

hm = str2double(get(hObject, 'String'));

if hm < 1 | hm > 10



end

hm = str2double(get(hObject, 'String'));

if isnan(hm)



end

66


data.hm = hm;


% --- Executes on button press in urbanarea.

function urbanarea_Callback(hObject, eventdata, handles)

% hObject handle to urbanarea (see GCBO)



% Hint: get(hObject,'Value') returns toggle state of urbanarea

set(handles.urbanarea, 'Value', 1);

set(handles.suburbanarea, 'Value', 0);

set(handles.openarea, 'Value', 0);

% --- Executes on button press in suburbanarea.

function suburbanarea_Callback(hObject, eventdata, handles)

% hObject handle to suburbanarea (see GCBO)



% Hint: get(hObject,'Value') returns toggle state of suburbanarea




% --- Executes on button press in openarea.

function openarea_Callback(hObject, eventdata, handles)

% hObject handle to openarea (see GCBO)



67

% Hint: get(hObject,'Value') returns toggle state of openarea




% --- Executes on button press in medium.

function medium_Callback(hObject, eventdata, handles)

% hObject handle to medium (see GCBO)



% Hint: get(hObject,'Value') returns toggle state of medium

set(handles.medium, 'Value', 1);

set(handles.freq200, 'Value', 0);


% --- Executes on button press in freq200.

function freq200_Callback(hObject, eventdata, handles)

% hObject handle to freq200 (see GCBO)



% Hint: get(hObject,'Value') returns toggle state of freq200




% --- Executes on button press in freq400.

function freq400_Callback(hObject, eventdata, handles)

% hObject handle to freq400 (see GCBO)

68



% Hint: get(hObject,'Value') returns toggle state of freq400




% --- Executes on button press in calculate.

function calculate_Callback(hObject, eventdata, handles)

% hObject handle to calculate (see GCBO)




value = get(handles.urbanarea, 'Value')& get(handles.medium, 'Value');

if value == 1

total = 69.55 + (26.16 * log10(data.freq))- (13.82 * log10( data.hb )) + (44.9 -

(6.55 * log10( data.hb ))) * log10(data.dist)...

- ((1.1 * log10(data.freq)-0.7)*(data.hm)-((1.56 * log10(data.freq)) - 0.8));

end

value = get(handles.urbanarea, 'Value')& get(handles.freq200, 'Value');

if value == 1



- ((8.29*(log10(1.54*(data.hm)))^2) - 1.1);

end

value = get(handles.urbanarea, 'Value')& get(handles.freq400, 'Value');

if value == 1



69

- ((3.2*(log10(11.75*(data.hm)))^2) - 4.97);

end

value = get(handles.suburbanarea, 'Value')& get(handles.medium, 'Value');

if value ==1

total = 69.55 + (26.16 * log10(data.freq))- (13.82 * log10(data.hb)) + (44.9 - (6.55

* log10(data.hb)))* log10(data.dist) - (5.4 + 2 * (log10(data.freq/28))^2)...


end

value = get(handles.suburbanarea, 'Value')& get(handles.freq200, 'Value');

if value == 1



- ((8.29*(log10(1.54*(data.hm)))^2) - 1.1);

end

value = get(handles.suburbanarea, 'Value')& get(handles.freq400, 'Value');

if value == 1



- ((3.2*(log10(11.75*(data.hm)))^2) - 4.97);

end

value = get(handles.openarea, 'Value')& get(handles.medium, 'Value');

if value ==1


* log10(data.hb)))* log10(data.dist) - (40.94 + (4.78 * (log10(data.freq))^2) - (18.33

* log10(data.freq)))...


end

value = get(handles.openarea, 'Value')& get(handles.freq200, 'Value');

if value ==1

70




- ((8.29*(log10(1.54*(data.hm)))^2) - 1.1);

end

value = get(handles.openarea, 'Value')& get(handles.freq400, 'Value');

if value ==1




- ((3.2*(log10(11.75*(data.hm)))^2) - 4.97);

end

set(handles.total, 'String', total);

% --- Executes on button press in reset.

function reset_Callback(hObject, eventdata, handles)

% hObject handle to reset (see GCBO)



initialize_gui(gcbf, handles);

function initialize_gui(fig_handle, handles)

data.freq = 0;

data.dist = 0;

data.hb = 0;

data.hm = 0;

setappdata(fig_handle, 'metricdata', data);

set(handles.freq, 'String', data.freq);

71

set(handles.dist, 'String', data.dist);

set(handles.hb, 'String', data.hb);

set(handles.hm, 'String', data.hm);

set(handles.total, 'String', 0);







% --- Executes on button press in pushbutton3.

function pushbutton3_Callback(hObject, eventdata, handles)

% hObject handle to pushbutton3 (see GCBO)




function total_CreateFcn(hObject, eventdata, handles)

% hObject handle to total (see GCBO)





usewhitebg = 1;

if usewhitebg


else


end

72

function total_Callback(hObject, eventdata, handles)

% hObject handle to total (see GCBO)



% Hints: get(hObject,'String') returns contents of total as text

% str2double(get(hObject,'String')) returns contents of total as a double

73

APPENDIX B – COMPARISONS OF VARIOUS TYPES OF MODEL

74

APPENDIX C – GLOSSARY OF TERMS

75

76

77