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A STATISTICAL PER CELL MODEL TUNING APPROACH FOR

CELLULAR NETWORKS

by

MUKUBWA WANYAMA EMMANUEL

Submitted in partial fulfilment of the requirements for the degree

MAGISTER TECHNOLOGIAE: ELECTRICAL ENGINEERING

Field of specialization: Telecommunication Technology

in the

Department of Electronic Engineering

FACULTY OF ENGINEERING

TSHWANE UNIVERSITY OF TECHNOLOGY

Supervisor: Mr. Anish Kurien

Co-Supervisor: Mr. Damien Chatelain

Co-Supervisor: Mr. Martin Menke Drewes

September 2006


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DECLARATION

I hereby declare that the dissertation submitted for the degree M Tech: Electrical

Engineering, at Tshwane University of Technology, is my own original work and has not

previously been submitted to any other institution of higher education. I further declare

that all sources are indicated and acknowledged by means of a comprehensive list of

references.

Name: Emmanuel Wanyama Mukubwa

Signature:

Copyright Tshwane University of Technology 2006


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DEDICATION

This thesis is dedicated to

My wife Purity andMy sons Gustave and the late Seth

For the perseverance and support they gave to me while away from home.

And

To the thousands of men and women who painstakingly helped me to answer the myriad

of questions that swirled through my head, I gratefully dedicate this dissertation. Some of

these men and women I had the pleasure of meeting them in person. Others I knew only

as a name on a book or a signature to a magazine article. But through speech or through

the printed word, each helped me to transmit my common-heritage, my civilization. I

fondly hope that in some slight measure I do likewise, and thus repay, in small part the

debt I owe my lecturers.


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ACKNOWLEDGEMENTS

I want to extend my gratitude and appreciation to:

My research study leaders at FSATIE, starting with Mr. Anish Kurien, forpointing out the importance of the effect of clutter and terrain on the signal

strength in built-up areas, together with Mr. Damien Chatelain whose technical

contributions and academic guidance were indispensable throughout the entire

duration of this study.

TUT for the financial support offered towards the research in this project and in

conference presentations.

COE for the financial support they offered towards the research in this project.

Mr. Martin Menke Drewes, for his cooperation and his expertise extended in

analysis and verification during the calibration and benchmarking of this project.


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ABSTRACT

Radio propagation prediction is one of the fundamental procedures in the nominal stages

of radio network planning. It is thus vital that radio propagation predictions are asaccurate as possible taking into account localized features. At times, the predicted and

measurement data for particular cells in the cellular network do not correlate. To resolve

this, various factors that influence radio propagation prediction in cellular communication

networks have to be analysed for each cell. Precise knowledge of these factors is vital in

radio propagation prediction. The approach taken in this study is to identify problematic

cells, characterize such cells taking into account factors that could influence the

inconsistencies, followed by the formulation of a method to tune a typical propagation

model to suit the problematic cell. This could provide a reliable method for the prediction

of results. The project seeks to identify problematic cells based on prediction data

obtained from a radio planning tool, ATOLL, as well as measurement data obtained from

the field. Each of the cells is then characterized based on its clutter and topographic data.

Based on the characteristics of the cell, a method is developed to train the propagation

model from which a correction factor is obtained for adjusting the propagation prediction

model to log the best predication. Although good results were obtained, the study was

limited by the accuracy of the measurement data obtained and inaccuracies in various

data components of the radio propagation prediction software. However, it is shown that

the proper analysis of the factors that impair propagated signals can greatly improve the

radio propagation prediction results. The developed prediction engine show a reduced

mean and standard deviation errors of -0.4508 & 4.0067, -0.5382 & 2.3628, -2.4936

&5.5662 and -0.8497 & 3.0843 respectively for the four sectors considered.


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TABLE OF CONTENTS

DECLARATION .............................................................................................................. 1

DEDICATION .................................................................................................................. 2ACKNOWLEDGEMENTS ............................................................................................. 3

PROLOGUE...................................................................................................................... 4

ABSTRACT....................................................................................................................... 5

TABLE OF CONTENTS ................................................................................................. 6

LIST OF FIGURES........................................................................................................ 11

LIST OF TABLES.......................................................................................................... 14

LIST OF TABLES.......................................................................................................... 14

CHAPTER 1: INTRODUCTION.................................................................................. 15

1.1. Background information ................................................................................... 15

1.2. Problem statement............................................................................................. 16

1.2.1 Sub-problem 1........................................................................................... 16

1.2.2 Sub-problem 2........................................................................................... 17

1.2.3 Sub-problem 3........................................................................................... 17

1.2.4 Sub-problem 4........................................................................................... 17

1.3. Hypotheses........................................................................................................ 17

1.3.1 Hypothesis 1.............................................................................................. 18

1.3.2 Hypothesis 2.............................................................................................. 18

1.3.3 Hypothesis 3.............................................................................................. 18

1.3.4 Hypothesis 4.............................................................................................. 18

1.4. Delimitations..................................................................................................... 19


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1.5. Research Methodology ..................................................................................... 19

1.6. Contribution of the study .................................................................................. 20

1.7. Brief overview of the Dissertation.................................................................... 20

CHAPTER 2: RADIO PROPAGATION ..................................................................... 23

2.1. Introduction....................................................................................................... 23

2.2. Wireless Channel .............................................................................................. 23

2.2.1 The Propagation Channel.......................................................................... 24

2.2.2 The Radio Channel ................................................................................... 25

2.2.3 The Modulation Channel .......................................................................... 252.2.4 The Digital Channel.................................................................................. 26

2.3. Propagation Phenomenon ................................................................................. 26

2.3.1 Diffraction................................................................................................. 27

2.3.1.1 The Huygens Principle ............................................................................ 28

2.3.1.2 The Fresnel Clearance Zone ..................................................................... 33

2.3.2 Scattering .................................................................................................. 36

2.3.3 Reflection.................................................................................................. 37

2.3.4 Penetration ................................................................................................ 37

2.3.5 Refraction.................................................................................................. 38

2.4. Radio Propagation Models................................................................................ 39

2.4.1 Free-Space Model ..................................................................................... 39

2.4.2 Plane Earth (Two-ray) Model ................................................................... 41

2.4.3 Curved Reflecting Surface Model ............................................................ 43

2.4.4 Land Propagation Models......................................................................... 45


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2.4.4.5 The Fading Model..................................................................................... 56

2.5. Conclusion ........................................................................................................ 58

CHAPTER 3: PREDICTION MODELS AND TUNING APPROACHES .............. 59

3.1. Introduction....................................................................................................... 59

3.2. Propagation Prediction Models......................................................................... 59

3.2.1 Okumura-Hata Model ............................................................................... 60

3.2.2 The COST 231-Hata Model...................................................................... 63

3.2.3 The COST 231 -Walfisch-Ikegami Model ............................................... 64

3.2.4 The Ibrahim-Parsons Method ................................................................... 683.2.5 The Lee Model.......................................................................................... 70

3.2.6 The ITU (CCIR) Model ............................................................................ 71

3.3. Model Tuning Approaches ............................................................................... 73

3.3.1 Statistical Tuning Approach ..................................................................... 73

3.3.2 Deterministic Tuning Approach ............................................................... 74

3.3.3 Semi-Statistical/Semi-Deterministic Tuning Approach ........................... 76

3.3.4 The Per Cell Tuning Approach................................................................. 77

3.4. Conclusion ........................................................................................................ 80

CHAPTER 4: CONDUCTING A MEASUREMENT-BASED RADIO PLANNING

STUDY............................................................................................................................. 82

4.1. Introduction....................................................................................................... 82

4.2. Model Tuning Measurement Collection........................................................... 82

4.2.1 Site and Clutter Selection.......................................................................... 83

4.2.2 MTM Data Capture................................................................................... 85


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4.2.3 MTM Data Conversion and Validation .................................................... 88

4.3. Using MTM Data to Calibrate ATOLL Propagation Models........................... 88

4.3.1 Digital Terrain Map Data Validation........................................................ 89

4.3.2 ATOLL Propagation Model Tuning......................................................... 89

4.3.3 Comparison of Predicted Signal to Measured Signal ............................... 96

4.4. Conclusion ........................................................................................................ 98

CHAPTER 5: DEVELOPMENT OF BUILDING AND VEGETATION

PROPAGATION MODELS .......................................................................................... 99

5.1. Introduction....................................................................................................... 995.2 Terrain Diffraction Factor Design .................................................................... 99

5.3 Building Correction Factor ............................................................................. 102

5.3.1 Defining Building Model Objectives...................................................... 103

5.3.2 Building Diffraction Model .................................................................... 103

5.4 Foliage Correction Factor Design Methodology and Planning ...................... 112

5.5 Additional Terrain Loss Correction Factor Design ........................................ 115

5.6 The Modified Propagation Prediction Model ................................................. 117

5.7 Propagation Prediction Engine Development................................................. 118

5.7.1 Building Blocks of the Propagation Prediction Engine .......................... 119

5.7.2 Prediction Algorithm Development........................................................ 120

5.7.3 Initiating a Prediction Session in MATLAB Ver.6.5 ............................. 121

5.8 Propagation Model Calibration and Validation.............................................. 121

5.9 Conclusion ...................................................................................................... 123

CHAPTER 6: RESULTS AND DISCUSSION .......................................................... 124


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6.1 Introduction..................................................................................................... 124

6.2 Benchmarking Procedure................................................................................ 124

6.2.1 Cases Considered in Model Validation................................................... 126

6.2.2 Comparative Analysis of Model Predictions and Measurements ........... 134

6.3 Conclusion ...................................................................................................... 142

CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS............................. 143

7.1 Objectives and Research Process.................................................................... 143

7.2 Summary of Findings...................................................................................... 144

7.3 Summary of Study Contributions ................................................................... 1457.4 Recommendations for Further Study.............................................................. 146

7.5 General conclusions ........................................................................................ 147

LIST OF REFERENCES............................................................................................. 148

APPENDIX A................................................................................................................ 154

APPENDIX B ................................................................................................................ 155

APPENDIX C................................................................................................................ 166

APPENDIX D................................................................................................................ 168


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LIST OF FIGURES

Figure 2.1: Wireless Channel Classification.................................................................... 24

Figure 2.2:Radio Wave Diffraction

. ................................................................................ 28Figure 2.3:Shadowing of Radio Waves by an Object...................................................... 29

Figure 2.4:Signal Levels on the Far Side of the Shadowing Object. .............................. 29

Figure 2.5: Representation of Radio Waves as Wavelets................................................. 30

Figure 2.6: Building of a new Wave Front by Vector Summation. .................................. 31

Figure 2.7: The Cornu Spiral. .......................................................................................... 32

Figure 2.8: The Fresnel Zone for a Radio Link. .............................................................. 34

Figure 2.9:Radio Wave Scattering. ................................................................................. 36

Figure 2.10:Radio Wave Reflection. ............................................................................... 37

Figure 2.11:Radio Wave penetration in to a building..................................................... 38

Figure 2.12:Radio Wave Refraction................................................................................ 39

Figure 2.13: Propagation over plane earth. .................................................................... 42

Figure 2.14: Propagation over curved reflecting surface................................................ 44

Figure 2.15: Knife-edge diffraction.................................................................................. 47

Figure 2.16:Diffraction over a cylinder. ......................................................................... 48

Figure 3.1:Definitions of Factors Neglected in Okumura- Hata Model. ........................ 61

Figure 3.2:Definition of the Parameters used in COST 231 - Walfisch-Ikegami Model. 65

Figure 3.3:Definition of the Street Orientation Angle ................................................. 66

Figure 4.1:Building Structure of Area Studied................................................................ 83

Figure 4.2: Trees on Straight Line Along the Street. ....................................................... 84

Figure 4.3:Digital Terrain Map of Studied Area. ........................................................... 86


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Figure 4.4: Scanned Map of the Region Studied. ............................................................. 86

Figure 4.5:ATOLL Model Calibration Process. ............................................................. 90

Figure 4.6:Initial Propagation Model Parameters. ........................................................ 92

Figure 4.7:Initial Best Server Coverage by Transmitter Array. ..................................... 93

Figure 4.8:Initial Best Server Coverage by Signal Level Array. .................................... 93

Figure 4.9: Calibrated Propagation Model Parameters.................................................. 94

Figure 4.10: Calibrated Best Server Coverage by Transmitter Array. ............................ 95

Figure 4.11: Calibrated Best Server Coverage by Signal Level Array. ........................... 95

Figure 4.12: Predicted/Measured Signal Strength Based on Default Model................... 96Figure 4.13: Predicted/Measured Signal Strength Based on Calibrated Model. ............ 97

Figure 4.14: The ATOLL Statistics Window..................................................................... 98

Figure 5.1: Theoretical Diffraction of Plane Waves over a Building. ........................... 104

Figure 5.2: Point Analysis Window as Displayed in ATOLL Planning Tool. ................ 116

Figure 5.3: Propagation Prediction Engine building blocks. ........................................ 120

Figure 6.1: First Sample Comparison of Measurement and Prediction........................ 125

Figure 6.2: Second Sample Comparison of Measurement and Prediction. ................... 125

Figure 6.3: Prediction Vs Measurement Based on Point to Point Analysis................... 127

Figure 6.4: Point to Point Analysis Error. ..................................................................... 127

Figure 6.5: Prediction Vs Measurement Based on Non-Linear Regression. ................. 129

Figure 6.6:Non-Linear Regression Error...................................................................... 130

Figure 6.7: Prediction Vs Measurement Based on Fixed Density. ................................ 131

Figure 6.8: Fixed Clutter and Terrain density Error. .................................................... 132

Figure 6.9: Prediction Vs Measurement based on variable Density. ............................ 133


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Figure 6.10: Variable Clutter and Terrain Density Error. ............................................ 134

Figure 6.11:Drive Test Routes of the Area under Study. .............................................. 135

Figure 6.12: T0377- Predicted Versus Measured Signal. .............................................. 136

Figure 6.13: T0377-Error between Measured and predicted signal. ............................ 137





Figure 6.18: T4658- Predicted Versus Measured Signal. .............................................. 141Figure 6.19: T4658-Error between Measured and predicted signal. ............................ 141


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LIST OF TABLES

Table 4.1: Site selection and classification. ..................................................................... 85

Table 4.2:Initial Propagation Model Parameters for Area Studied

. .............................. 92Table 4.3: Calibrated Propagation Model Parameters for Area Studied........................ 94

Table 5.1:A Sample of the Diffraction Loss Estimates.................................................. 101

Table 5.2:Averaged Values for the Terrain Exponent................................................... 102

Table5.3: Some of the Clutter Data Used in this Study.................................................. 106

Table 5.4:Building Densities for Region under Study................................................... 112

Table 5.5: Vegetation Loss Data from Field. ................................................................. 114

Table 5.6: Vegetation Densities for Region under Study. .............................................. 115

Table 5.7:A Sample of the Additional Diffraction Loss data. ....................................... 117

Table 6.1:Initial K-Parameters for Non-Linear Regression. ........................................ 128

Table 6.2: Calibrated K-Parameters for Non-Linear Regression.................................. 128

Table 6.3: K-Parameters used in Fixed Clutter and Terrain density............................. 131

Table 6.4: K-Parameters for Variable Clutter and Terrain density. ............................. 133

Table 6.5: Suburban Cell Sites Considered. .................................................................. 135


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CHAPTER 1: INTRODUCTION

1.1. Background information

Cellular system design has become more challenging in recent years. Increased

competition among operators requires higher levels of performance. Site acquisition is

problematic because of the limited availability of suitable sites and due to the fact that

neighbouring residents generally demand increasingly unobtrusive installations. To meet

these design challenges, engineers must have a "tool box" of techniques for maintaining

design integrity and minimizing capital expenditure. The three dimensions of system

performance of most interest to a network operator are coverage, capacity and

interference [32] (Lempiinen & Manninen, 2001:28). Advanced prediction tools use

digital terrain and clutter databases to generate predictions of signal strength throughout

the coverage area [4] (Parsons, 2000:375). Many radio propagation prediction tools are

developed to take into account propagation prediction algorithms that not only emulate

the real environment, but also help planning engineers to cope with situations where all

the information necessary for prediction is not always available. With the complexity of

cellular networks and the relative lack of specialists, the radio propagation prediction

process becomes difficult. The provision of a radio planning tool with standard correction

factors for particular cell site characteristics could greatly save the inexperienced radio

planners from analysis of clutter and terrain in the determination of correction factors for

particular problematic cells. Thus, the development of standard correction factors on per-

cell basis could be of great importance in cellular network planning.


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1.2. Problem statement

To design a cellular network for a particular region efficiently and accurately, precise

prediction of the received radio signal is a crucial step. While terrain has a profound

effect on the propagation of radio signals (especially at higher frequencies), more

localized features of the environment such as trees and structures (buildings, houses, etc.)

can also have a substantial impact on propagation. The received signal prediction

accuracy depends to a great extent on the level to which these localized features in the

area under study are taken into consideration by the prediction method. The major

problem for the region under consideration, which is characterized as hill type of

environment with mixed trees and structures, is the estimation of the effect of terrain

type, trees and constructions on the total path loss between a transmitter and a receiver.

This research work attempts to model the localized features, develop, test and optimize a

radio propagation model that takes into consideration the hilly terrain, vegetation and

constructions of the region. Consequently, a standard correction factor is established for

each cell which can be applied to other cells with similar characteristics.

The following sub-problems were identified and formed the basis of the study.

1.2.1 Sub-problem 1

To conduct a comparative study of coverage prediction and field measurement data of

cells in a cellular network. Based on this study, the problematic cells are identified and

characterized based on the factors that influence propagation of radio signals in each cell.


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This would include a critical look at how terrain and land use / land cover factors

influence radio propagation.

1.2.2 Sub-problem 2

To test various propagation predictions models and select one which gives least deviation

between the predicted and the measured data. This model would be tuned to log the best

prediction relative to the measured data.

1.2.3 Sub-problem 3

To formulate a method based on the problematic cell characteristics and tune the

propagation prediction model to suit the problematic cell.

1.2.4 Sub-problem 4

To establish standard correction factors as per the tuning results. These could be used by

inexperienced cellular network planners in areas with similar characteristics as the cell

under study.

1.3. Hypotheses

From the above sub-problems, the following hypotheses were formed.


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1.3.1 Hypothesis 1

It is possible to develop a clear understanding of the factors that influence radio

propagation by conducting a comparative study of predicted coverage and field

measurements of a cellular network. It is assumed that a number of factors will be

generated based on different terrain types and land use / land cover types.

1.3.2 Hypothesis 2

A number of standard propagation prediction models are available for evaluation. Anappropriate propagation prediction model is selected for further tuning.

1.3.3 Hypothesis 3

A methodology is developed to facilitate the process of tuning the propagation prediction

model based on the cell characteristics. This methodology is developed based on the

terrain and clutter types.

1.3.4 Hypothesis 4

Once the tuning is complete, correctional factors are extracted from these results for

terrain and clutter types as well as model coefficients. These correctional factors are then

verified by using them on cells with similar characteristics upon which they are adopted

as standard correctional factors.


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1.4. Delimitations

This study is not intended to replace the role of expert planners but to act as an

added tool in the propagation prediction process for less experienced planners.

Though most crucial characteristics of the cell are taken into consideration, there

are some minor characteristics which are assumed hence some of the factors

derived from this study might be inaccurate to some extend.

The formulation arrived at in this study can only be applied to cells with similar

characteristics as the ones under consideration.

The unavailability of diffraction coefficients for many indoor structures may also

compromise the accuracy of the study results.

1.5. Research Methodology

The research methods employed in this study were both quantitative as well as

experimental in nature and consisted of four phases. The first phase consisted of

conducting a comparative analysis of the prediction and field measurement data to

identify problematic cells. The characteristics of the problematic cell were then analysed

and modelled. Field measurements were consequently done to establish the contribution

of each cell characteristic to the path loss and used to calibrate the propagation model.

Lastly the calibrated model was simulated and the results compared with the measured

data and minor adjustments conducted where necessary. From the above phases,

correctional factors relative to the cell under consideration were extracted for the cell.


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1.6. Contribution of the study

The potential benefit of the per cell model tuning is to improve the quality of the network

as it considers a small section of the network and in more detail. The provision of well

calculated correctional factors for cells with particular characteristics makes it possible

for inexperienced network planners to accomplish their tasks. The defined correctional

factors may be used to characterize new cells where no real data for the new region is

available. The integration of this method into a statistical model provides a model that

approximates closely to the physical environment under consideration for both macro and

micro cells compared to their individual capabilities.

1.7. Brief overview of the Dissertation

The following section gives a brief overview of how the report is presented.

1. Background of the Project- This chapter gives the background of the project and define

the statement problem. It also gives the hypothesis and methodology to be used.

2. Literature Review on Path Loss Model Theory This chapter covers an important

portion of the research whereby basic principles of radio wave propagation and

applicable laws of physics are established.


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3. Theoretical Consideration of Mathematical Models In this chapter, an overview of

selected propagation models and prediction methods in the latest literature is given as key

to a good identification of a model design approach likely to give more reliable results.

4. Conducting a Measurement-based Propagation Prediction Study This chapter

presents the preliminary steps towards the actual model design, from field measurements

collection to updating and validating the computational databases in the existing

propagation prediction system (ATOLL planning tool). Furthermore a thorough

calibration process of the ATOLL propagation model using the field measurements ispresented in this chapter.

5. Development of Terrain and Clutter Model This chapter presents the objective,

design methodology and procedures of the proposed propagation model and the

supporting prediction engine for testing purposes. The main components of the entire

propagation engine are presented and their inter-working mechanism with the proposed

model is described. The model computational rules used in the MATLAB algorithm as

well as the model testing, optimization and validation method are explained.

6. Results and Discussions In this chapter, the results of the study are presented and a

benchmark-based discussion is made with reference to the comparison between the

measurements and predictions from the proposed model.


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7. Conclusions and Recommendations In this chapter, an overview of the robustness

and validity of the proposed model is given with respect to applicable area of the study.

The achievement of the set goals is quantified and a number of recommendations for

future work are given.


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CHAPTER 2: RADIO PROPAGATION

2.1. Introduction

The radio channel places fundamental limitations on performance of mobile

communication system. The transmission path between the transmitter and the receiver

can vary from simple direct line of sight to one that is severely obstructed by buildings

and foliage [1] (Gibson, 1997:1182). Thus, there is a significant incentive to devise

engineering tools that can accurately and efficiently design and plan such systems. In this

chapter, mobile radio propagation is described using appropriate statistical and

deterministic techniques. This chapter covers the wireless channel and more so the

propagation channel and models of the impairments a radio signal encounters as it

propagates from the transmitter to the receiver.

2.2. Wireless Channel

A wireless mobile channel is modelled as a time-varying communication path between

two stations such as from one terminal to another terminal. The first terminal is the fixed

antenna at a base transceiver station (BTS), while a moving mobile station (MS) or a

subscriber represents the second terminal. This becomes a multi-path propagation

channel with fast fading. Hence propagation in a multi-path channels depends on the

actual environment, such as the antenna height, the profile of the buildings, the trees, the

roads, and the terrain [8] (Agrawal and Zeng, 2003: 59) [36] (Aguiar and Gross, 2003).

Figure 2.1 represents the most commonly referenced channels to clarify different notions

related to the concept of wireless channels in digital communication systems.


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Figure 2.1: Wireless Channel Classification.

2.2.1 The Propagation Channel

The propagation channel lies between the transmitter and receiver antennas and is

influenced only by the phenomena that influence the propagation of electromagnetic

waves. It is almost always linear and reciprocal, and hence, these characteristics will be

assumed. The phenomena of this channel only effect the attenuation of the transmitted

signal and, therefore, this channel has a multiplicative effect on the signal. The signal

transmitted consists of the information modulated on top of the carrier frequency [36]

(Aguiar and Gross, 2003).

0100100100111010011010 0100100100111010011010

Base bandsymbols

Base bandsymbols

Digital/analogue

Modulator

IF/FR stages IF/FR stages

Demodulator

Digital/analogue

Transmitter Receiver

Packets

Bits

Antenna Antenna

Radio channel

Propagation channel

Modulation channel

Digital channel


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2.2.2 The Radio Channel

The radio channel consists of the propagation channel and both the transmitter and

receiver antennas. As long as the antennas are considered to be linear, bilateral and

passive, the channel is also linear and reciprocal. The signal is only affected by

attenuation, but the attenuation of the propagation channel might be different depending

on antennas used, where the antenna influence is strictly linear. The signal transmitted is

the same as with the propagation channel but might be scaled by the use of antennas [36]


2.2.3 The Modulation Channel

The modulation channel consists of the radio channel plus all system components (such

as amplifiers and different stages of radio frequency circuits) up to the output of the

modulator on the transmitter side and the input of the demodulator on the receiver side.

The linearity of the system depends on the transfer characteristics of the components

between demodulator or modulator and the antennas. The channel is non-reciprocal

because amplifiers (the system component added to the radio channel) are considered to

be non-reciprocal. Due to the amplification of the received signal at this point, additive

effects damaging the signal come into play. These include noise and interference. Some

of these additive effects might already be present in the radio channel; however, noise

from electric circuits is added at this channel level. Hence, complete characterization of

the additive effects can not be done at the radio channel level. The signal consists of base


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band symbols which are modulated on top of the carrier frequency (refer to next section)

[36] (Aguiar and Gross, 2003).

2.2.4 The Digital Channel

The digital channel consists of the modulation channel plus the modulator and

demodulator. It relates the digital base band signal at the transmitter to the digital signal

at the receiver, and describes the bit error patterns. The channel is non-linear and non-

reciprocal. At this channel level, no further effects come into play. Instead, the corrupted

signal is interpreted at this level as a bit sequence. If the signal has been corrupted too

heavily, the interpreted bit sequence differs from the true bit sequence intended to be

conveyed. The inputs to this channel are bit streams, which might stem from information

packets. The bits are grouped and then turned into analogue representations, referred to as

symbols. These symbols belong to the base band. This analogue signal is then passed to a

modulator which modulates the base band signals on top of the carrier frequency [36]


2.3. Propagation Phenomenon

Propagation mechanisms are very complex and diverse. Firstly, because of the separation

between the receiver and the transmitter, attenuation of the signal strength occurs. In

addition, the signal propagates by means of diffraction, scattering, reflection,

transmission, refraction, etc [37] (Neskovic, Neskovic and Paunovic, 2002). These

mechanisms renders propagation phenomenon to be non-line-of-sight and hence impairs

direct signals from the transmitter to the receiver. This means that the signal from the


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transmitter arrives at the receiver from various directions with different time delays. This

results in multi-path effects or fading of the signal as well as the problem of reception due

to different time delays.

2.3.1 Diffraction

Diffraction occurs when the direct line-of-sight (LoS) propagation between the

transmitter and the receiver is obstructed by an opaque obstacle whose dimensions are

considerably larger than the transmitted signal wavelength. The diffraction occurs at the

obstacle edges where part of the wave appears to bend into shaded areas behind the edge ,

and as a result, they are additionally attenuated. The diffraction mechanism allows the

reception of radio signals when the LoS conditions are not satisfied (non-LoS case),

whether in urban or rural environments [1] (Gibson, 1997:1183) [37] (Neskovic,

Neskovic and Paunovic, 2002) [8] (Agrawal and Zeng, 2003: 60). This is well illustrated

in figure 2.2;


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Figure 2.2:Radio Wave Diffraction.

2.3.1.1The Huygens Principle

Refraction and reflection of radio waves are mechanisms which are fairly easy to picture,

but diffraction is much less intuitive. To understand diffraction and radio propagation in

general, it is very helpful to have an understanding of how radio waves behave in an

environment which is not strictly "free space". Consider figure 2.3, in which a wave front

is travelling from left to right, and encountering an obstacle which absorbs or reflects

most of the incident radio energy. Assume that the incident wave front is uniform; i.e., if

we measure the field strength along the line A-A, it is the same at all points. To quantify

the field strength along a line B-B on the other side of the obstacle, we provide an axis

in which zero coincides with the top of the obstacle, and negative and positive numbers

denote positions above and below this, respectively (The parameter used on this axis is

defined later) [49] (McLarnon, 1997).

TransmitterReceiver

Radiowaves


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Figure 2.3:Shadowing of Radio Waves by an Object.

The behaviour of the signal after the obstacle can be graphically visualized as in figure

2.4.

Figure 2.4:Signal Levels on the Far Side of the Shadowing Object.

Advancing wavefront

A B

A B

-2

-1

0

1

2

3


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The explanation for the non-intuitive behaviour of radio waves in the presence of

obstacles in their path is described in Huygens Principle[4] (Parsons, 2000: 33-34). This

suggests that each point on a wave front acts as a source of a secondary wave-front

known as a wavelet, and a new wave-front is then built up from the combination of the

contributions from all of the wavelets on the preceding wave-front.

Figure 2.5:Representation of Radio Waves as Wavelets.

The secondary wavelets do not radiate equally in all directions - their amplitude in a

given direction is proportional to (1 + cos ), where is the angle between that direction

and the direction of propagation of the wave-front. The amplitude is therefore maximum

in the direction of propagation and zero in the reverse direction. The representation of a

wave front as a collection of wavelets is shown in Figure 2.5 [49] (McLarnon, 1997). At

Radio energy fromwavelets entersshadowed region


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a given point on the new wave-front (point B), the signal vector (phasor) is determined by

vector addition of the contributions from the wavelets on the preceding wave front, as

shown in Figure 2.6 [49] (McLarnon, 1997). The largest component is from the nearest

wavelet, and we then get symmetrical contributions from the points above and below it.

These latter vectors are shorter, due to the angular reduction of amplitude described

above, and also the greater distance travelled. The greater distance also introduces more

time delay, and hence the rotation of the vectors as shown in figure 2.6.

Figure 2.6: Building of a new Wave Front by Vector Summation.

As we include contributions from points farther and farther away, the corresponding

vectors continue to rotate and diminish in length, and they trace out a double-sided spiral

path, known as the Cornu spiral[49] (Hall et al as quoted in Mclarnon, 1997).

A

B

+

A

+2

+1

0

-2

-3

Vector -2

Vector -1

Vector 0

Vector sum

Vector +1

Vector +2


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Figure 2.7: The Cornu Spiral.

The Cornu spiral, shown in figure 2.7, provides the tool we need to visualize what

happens when radio waves encounter an obstacle. In free space, at every point on a new

wave-front, all contributions from the wavelets on the preceding wave-front are present

and un-attenuated. So, the resultant vector corresponds to the complete spiral (i.e., the

endpoints of the vector are X and Y) [49] (Hall et al as quoted in Mclarnon, 1997).

Considering the situation shown in figure 2.3, each location on the wave front B-B,

visualize the makeup of the Cornu spiral (note that the top of the obstacle is assumed to

be sufficiently narrow that no significant reflections can occur from it). At position 0,

level with the top of the obstacle, we will have only contributions from the positive half

of the preceding wave-front at A-A, since all of the others are blocked by the obstacle.

Therefore, the received components form only the upper half of the spiral, and the

resultant vector is exactly half the length of the free space case, corresponding to a 6 dB


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reduction in amplitude. As we go lower on the line B-B, we start to get blockage of

components from the positive side of the A-A wave-front, removing more and more of

the vectors as we go, and leaving only the tight upper spiral. The resulting amplitude

diminishes monotonically towards zero as we move down the new wave front. But, there

is still signal present at all points behind the obstacle [49] (Mclarnon, 1997).

To explain the mysterious ripples on graph points along line B-B above the obstacle,

looking at the Cornu spiral again, as we move up the line, we begin to add contributions

from the negative side of the A-A wave front (vectors -1, -2, etc.). By observing theeffect on the resultant vector, as we make the first turn around the bottom of the spiral, it

reaches its maximum length, corresponding to the highest peak in the graph of Figure 2.4.

As we continue to move up B-B and add more components, we swing around the spiral

and reach the minimum length for the resultant vector (minimum distance from point Y).

Further progression up B-B results in further motion around the spiral, and the

amplitude of the resultant oscillates back and forth, with the amplitude of the oscillation

steadily decreasing as the resultant converges on the free space value, given by the

complete Cornu spiral (vector X-Y) [49] (Mclarnon, 1997).

2.3.1.2The Fresnel Clearance Zone

A Fresnel zone is the volume of space enclosed by an ellipsoid, which has two antennas

at the ends of a radio link at its foci [4] (Parsons, 2000). The two-dimensional

representation of a Fresnel zone is shown in Figure 2.8 [49] (McLarnon, 1997). The

surface of the ellipsoid is defined by the path ACB and exceeds the length of the direct


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path AB by some fixed amount. This amount is n/2, where n is a positive integer. For

the first Fresnel zone, n = 1 and the path length differs by l/2 (i.e., a 180 phase reversal

with respect to the direct path). For most practical purposes, for NLOS, only the first

Fresnel zone needs to be considered.

Figure 2.8: The Fresnel Zone for a Radio Link.

A radio path has first Fresnel zone clearance if, as shown in Figure 2.8, no objects

capable of causing significant diffraction penetrate the corresponding ellipsoid. We then

recall how we constructed the wave-front behind an object by vector addition of the

wavelets comprising the wave-front in front of the object, and apply this to the case

where we have exactly first Fresnel zone clearance. We wish to find the strength of the

direct path signal after it passes the object [49] (Hall et al as quoted in Mclarnon, 1997).

Assuming there is only one such object near the Fresnel zone, we can look at the resultant

wave-front at the destination point B. In terms of the Cornu spiral, the upper half of the

B

A

C

Bd

d1 d2


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spiral is intact, but part of the lower half is absent, due to blockage by the object. Since

we have exactly first Fresnel clearance, the final vector to be added to the bottom of the

spiral is 180 out of phase with the direct-path vector - i.e., it is pointing downwards.

This means that we have passed the bottom of the spiral and are on the way back up, and

the resultant vector is near the free space magnitude (a line between X and Y in Figure

2.7). In fact, it is sufficient to have 60% of the first Fresnel clearance, since this will still

give a resultant that is very close to the free space value [4] (Parsons, 2000). In order to

quantify diffraction losses, they are usually expressed in terms of a dimensionless

parameter v, given by the following expression [4] (Parsons, 2000).

dv

= 2

(2.1)

Where dis the difference in lengths of the straight-line path between the endpoints of

the link and the path which just touches the tip of the diffracting object, that is d= (d1 +

d2-d) as in figure 2.8. By convention, v is positive when the direct path is blocked (i.e.,

the obstacle has positive height), and negative when the direct path has some clearance

("negative height"). When the direct path just grazes the object, v = 0. Since in this

section we are considering LoS paths, this corresponds to specifying that is negative (or

zero). For first Fresnel zone clearance, we have d= /2, so from equation (2.1), v = -1.4.

From figure 2.4, we can see that this is more clearance than necessary. In fact, we get

slightly higher signal level (and path loss less than free space value) if we reduce the

clearance to v = -1, which corresponds to d= /4. The (v = -1) point is also shown on

the Cornu spiral in Figure 2.7. Since d= /4, the last vector added to the summation is


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rotated 90 from the direct-path vector, which brings us to the lowest point on the spiral.

The resultant vector then runs from this point to the upper end of the spiral at point Y. It

is shown that this vector is a bit longer than the distance from X to Y (we have a slight

gain of about 1.2 dB over the free space case) and that 60% of the first Fresnel Zone

clearance (v = -0.85) can be secured without suffering significant loss [49] (Mclarnon,

1997).

2.3.2 Scattering

Scattering occurs when the propagation path contains obstacles whose dimensions are

comparable to the wavelength. The nature of this phenomenon is similar to the diffraction

except that the radio waves are scattered in a greater number of directions. Of all the

effects mentioned, scattering is the most difficult to predict [1] (Gibson, 1997:1183) [37]

(Neskovic, Neskovic and Paunovic, 2002) [8] (Agrawal and Zeng, 2003: 60). This is

illustrated in the figure 2.9.

Figure 2.9:Radio Wave Scattering.

xT


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2.3.3 Reflection

Reflection occurs when the radio wave impinges the obstacle whose dimensions are

considerably larger than the wavelength of the incident wave. A reflected wave can either

decrease or increase the signal level at the reception point. In cases where many reflected

waves exist, the received signal level tends to be very unstable. This phenomenon is

commonly referred to as multi-path fading, and the signal is often Rayleigh distributed

[1] (Gibson, 1997:1183) [37] (Neskovic, Neskovic and Paunovic, 2002) [8] (Agrawal and

Zeng, 2003: 60). This is shown in the figure 2.10.

Figure 2.10:Radio Wave Reflection.

2.3.4 Penetration

Penetration occurs when the radio wave encounters an obstacle that is to some extent

transparent for the radio waves. This mechanism allows the reception of radio signals

inside buildings as shown in figure 2.11 in cases where the actual transmitter locations

are either outdoors or indoors [3] (Hess, 1998: 181) [37] (Neskovic, Neskovic and

Paunovic, 2002).

xT


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Figure 2.11:Radio Wave penetration in to a building.

2.3.5 Refraction

Since the refractive index of the atmosphere is not constant, the radio waves do not

propagate along a straight line, but rather along a curved one. Therefore, the coverage

area of an actual transmitter is usually larger. However, as a result of the fluctuations of

the atmosphere parameters, the received signal strength level fluctuates as well. This

needs to be considered in macro-cell radio system design [4] (Parsons, 2000:26-31) [37]

(Neskovic, Neskovic and Paunovic, 2002). The concept is illustrated in figure 2.12;

xT


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Figure 2.12:Radio Wave Refraction.

2.4. Radio Propagation Models

As the radio waves travel from the transmit antenna to the receive antenna, they suffer

attenuation due to propagation loss [38] (Communication research centre, 2005). This

loss can be modelled using a variety of methods, some of which are discussed below.

2.4.1 Free-Space Model

The power received Pr by an antenna of gain Gr due to a source ofPt watts and antenna

gain Gt at wavelength and free space distance d is given by the Friis transmission

formula [3] (Hess, 1998: 157):

Signals with increasingfrequency

Signals pass into theouter space

F2 Layer

F1 Layer

E Layer

D layer

Ionosphere

Earth

Stratosphere


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[ ]24 dGGPP rttr = (2.2)

Since wavelength equals the speed of propagation divided by frequency, the propagation

loss (or path loss) is conveniently expressed as a positive quantity and equation (2.2) can

be rewritten as [4] (Parsons, 2000:16-17):

)(log10 10 rtF PPL dB =

kdfGG KMMHZrt +++= )(log20)(log20log10log10 10101010

(2.3)

Where ( )810 1034log20 = k ? It is often useful to compare path loss with the basic

path loss between isotropic antennas [4] (Parsons, 2000:21-22);

4.32)(log20)(log20 1010 ++= KMMHZdB dfL

(2.4)

The relations in equation (2.4) do not apply to small path lengths. For applicability, the

transmitting antenna must be located in the far field of the receiving antenna. A

commonly applied criterion is )2( 2 add , where ad is the major antenna dimension?

This criterion is based on limiting the phase difference at distance dover a plane to one-

sixteenth of the wavelength [3] (Hess, 1998: 157) [39] (Mishra, 2004: 27).


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2.4.2 Plane Earth (Two-ray) Model

In a practical mobile channel, a single direct path between the base station and the mobile

seldom exists, and hence, the free space propagation model is of little use. The two-ray

reflection model shown in the figure 2.13 is a useful propagation model based on

geometrical optics and considers both the direct and ground reflected propagation path.

This model assumes that the wavelength is much smaller than the dimensions of any

obstacle encountered in the propagation channel.

The total received electromagnetic field rE is the resultant of direct line of sight

component LOSE and a ground reflected component gE , and is referenced to an

electromagnetic field measured over a small distance do. From figure 2.13, th is the

height of the transmitter and rh is the height of the receiver. According to the laws of

reflection,

0 =i and iEE =0

(2.5)


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Figure 2.13: Propagation over plane earth.

Where is the reflection coefficient for the ground? As i approaches 0o the reflected

wave is equal in magnitude and 180o out of phase with the incident wave. It can be shown

that, the received field in volts per meter is;

)2sin()2( 0 ddEE LOSr

(2.6)

Where the phase difference is related to the path difference d between the direct

and ground reflected paths and is given by;

)2( d= (2.7)

At large values ofd,

)(receiverRx

d

th

rh

)( rtransmitteTx

LOSE

iE

gEE =0

i 0

gLOSr EEE +=


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)2()2()2sin( dhh rt =

(2.8)

and the received electric field in volts per meter is given by

)2(20

ddhhEE rtLOSr

(2.9)

The power received at dis related to the square of the electric field and can be expressed

approximately as

)( 422 dhhGGPP rtrttr =

(2.10)

For large distances, the received power drops at a rate of 40dB per decade. The received

power and path loss become independent of frequency (fourth power distance law.) The

path loss in decibels for the two-ray model is approximated as given below [1] (Gibson,

1997:1184-1186)

dhhGGPL rtrtdB 1010101010 log40log20log20log10log10 +=

(2.11)

2.4.3 Curved Reflecting Surface Model

The above model is only considered for distances less than a few tens of kilometres.

However, for long distances, the earths curvature needs to be considered. The case of


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two visible antennas sited on smooth earth of effective radius reis illustrated in the figure

2.14 below.

Figure 2.14: Propagation over curved reflecting surface.

The heights of the antenna above the earths surface are th and rh . The antenna heights

above the tangent plane through the point of reflection are 'th and'rh . If dE is the field

strength at the receiving antenna due to direct wave, the total received field Eis given by

)]](exp[1[ += jEE d

(2.12)

Where is the reflection coefficient of the earth and jexp= , ]4[ '' dhh rt = ;

Thus;

)]}(exp[1{ += jEE d

(2.13)

xT xR 1r

2r 'rh

'th

th rh 1d 2

d

er


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This equation can be used to calculate the received field strength at any location, but the

curvature of the spherical earth produces a certain amount of divergence of the ground

reflected wave. This is corrected by multiplying the value of for a plane surface by

divergence factorD given by the following formula [4] (Parsons, 2000:21-22)

21''21 ]})()2([1{

++ rte hhrddD

(2.14)

2.4.4 Land Propagation Models

A land mobile radio channel is characterized by a multi-path propagation channel with

fading. The signal reaches the destination using many paths as a result of diffraction,

scattering, reflection, transmission, and refraction from various objects along the path of

propagation. The signal strength and quality of received radio waves also varies

accordingly as the time to reach the destination changes. This implies that the wave

propagation in a multi-path channel depends on the actual environment, including factors

such as the antenna height, profiles of buildings, roads, and terrain. Therefore, we need to

describe the behaviour of mobile radio channels using a good and relevant statistical

mechanism. Hence, the received signal power is expressed as follows.

][ LPGGP trtr =

(2.15)


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Where L represents the propagation loss in the channel? Wave propagation in a mobile

radio channel is characterised by three aspects namely path loss, slow fading, and fast

fading. Therefore,L can be expressed as follows.

fsp LLLL =

(2.16)

Where pL , sL , and fL represent the path loss, slow fading loss, and fast fading loss,

respectively [8] (Agrawal and Zeng, 2003: 62-63)?

2.4.4.1 Diffraction Models

The real world propagation paths often involve obstructions like trees, buildings, and

terrain. The additional loss associated with such obstructions is called diffraction loss.

To cater for this situation, the following models were formulated [3] (Hess, 1998: 162);

2.4.4.1.1 Knife-Edge Diffraction Model

When the free-space condition is not satisfied, one means of quantifying the additional

path loss is to treat the obstacle as a diffracting knife-edge [3] (Hess, 1998: 164). The

diffraction path loss in this case can be readily estimated using classical Fresnel solution

for the field behind a knife-edge or half plane. Figure 2.15 below illustrates this

approach.


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Figure 2.15: Knife-edge diffraction.

The field strength at a receiver point xR in the shadowed region is a vector sum of the

fields due to all of secondary Huygens sources in the plane above the knife-edge. The

field strength dE of a knife-edge diffracted wave is given by the following formula.

+==

v

d dttjjEvFEE )2exp(]2)1([)(2

00

(2.17)

Where E 0 is the free space field strength in the absence of the knife-edge and F (v) is the

complex Fresnel integral which is a function of the Fresnel-Kirchoff diffraction

parameter v.

2121 )(2 ddddhv +=

(2.18)

Where h is the knife-edge height, d1 andd2 are the distances of the knife-edge from the

transmitter and the receiver respectively. The diffraction gain in decibels due to the

xR xT

h

1d 2d


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presence of knife-edge is given by the following expression [40] (Wireless

Communication, 1996) [4] (Parsons, 2000:36-39).

)(log20 10 vFGd =

(2.19)

2.4.4.1.2 Rounded-Edge Diffraction Model

Real-world obstructions are seldom as abrupt as knife-edges; hence a diffraction solution

wherein the knife-edge is replaced with a cylinder of radius R is of interest. Such a

solution can be given in terms of the dimensionless parameter:

21

21

2131

61

+

=

dd

ddR

(2.20)

Where 1d and 2d are as shown in the figure 2.16 below.

Figure 2.16:Diffraction over a cylinder.

xT xR

1d 2d

r

d


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The diffraction loss in decibels is the sum of the usual knife-edge diffraction loss in

decibels plus curvature and correction losses in decibels [3] (Hess, 1998: 166) and is

given approximately by the following formula:

2,7.66)1(log)5.236.43(

4.1,75.063.302.219.76

10

432


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edge. Here, the losses due to each obstruction / knife-edge are evaluated in the absence

of the others. The edge yielding the highest loss is termed as the main edge. The

diffraction losses over the remaining edges are then found with respect to the main edge

and the visible transmitter and receiver. For more than three knife-edges, the total loss is

set to the sum of the individual losses for edges in the order of decreasing loss. The above

procedure is conducted recursively [3] (Hess, 1998: 166-167) [9] (Holbeche, 1985: 17-

18). The Edwards and Durkin method is identical to that of Epstein-Peterson for up to

three obstacles. For four or more obstacles, they construct a Bullington-like path between

the outer two obstacles. This method is more accurate than Bullington and requires atmost three diffraction calculations [3] (Hess, 1998: 167).

The Bullington method produces results that underestimate the path loss. The Epstein-

Peterson and Japanese methods are better when considering three or more obstacles but

provide path loss predictions that are too low. The Deygout method shows good

agreement with the rigorous theory for two edges, but overestimates the path loss in

circumstances where the other methods produce underestimates. The pessimism of the

Deygout method increases as the number of obstructions is increased; hence calculations

are often terminated after consideration of three edges. Giovaneli devised an alternative

technique which remains in good agreement with values obtained by Volger even when

several obstructions are considered [4] (Parsons, 2000: 50-52).


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2.4.4.2The Scattering Model

The measured path loss in a mobile radio environment is often less than what is predicted

by reflection and diffraction alone. This is because when a radio wave impinges on a

rough surface, the reflected energy is spread out (diffused) in all directions due to

scattering. The roughness of a surface is often tested using the Rayleigh criterion, which

defines a critical height ch of surface protuberances for a given angle of incidence i as

follows.

]cos8[ ich =

(2.22)

A surface is considered smooth if its minimum to maximum protuberance h is less than

ch and is considered rough if the protuberance is greater than ch . For rough surfaces, the

reflection coefficient needs to be modified by a scattering loss factor to account for

diminished specularly reflected field.

])cos(8exp[ 2 ihs =

(2.23)

Where h is the standard deviation of the surface height about the mean surface height?

To give better agreement with the measured results this was modified to;

])cos(8[])cos(8exp[ 202 ihihs I=

(2.24)


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Where Io is the Bessel function of the first kind and zeroth order [1] (Gibson, 1997: 1187-

1188)? Analysis based on the geometric theory of diffraction and physical optics can be

used to determine the scattered strength. For urban mobile radio system, models based on

a bistatic radar equation may be used to compute the scattering losses in the far field. The

radar cross section (RCS) of a scattering object is defined as the ratio of the power

density of the signal scattered in the direction of the receiver to the power density of the

radio wave incident upon the scattering object and has units of square meters. The bistatic

radar equation (2.25) describes the propagation of a wave travelling in free-space and

intercepted by a scattering object, and then radiated in the direction of the receiver,

( ) ( ) ( ) ( ) 4log30log20)( 102

10 +++= dBmRCSdBiGdBmPdBmP ttr

rt dd 1010 log20log20

(2.25)

Where td and rd are the distance from the scattering object to the transmitter and

receiver, respectively. This model can only be applied to scattered waves in the far field

of both the transmitter and the receiver [1] (Gibson, 1997: 1187-1188).

2.4.4.3The Penetration Model

The penetration loss of a signal depends on a number of factors. Central among them is

the carrier frequency, the propagation condition along the path and the height of the

receiver within the building. However, there are other influencing factors which include

the orientation of the building with respect to the base station, the building construction

and the internal building layout [4] (Parsons, 2000: 192). A simple two-parameter model


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is used to calculate building penetration loss. When a ray penetrates a building, it first

suffers losses due to the external wall. But between the external wall and the prediction

point there is additional distance dependent losses which have to be also added.

Building penetration loss can be quantified as follows. First, a number of local mean

values are established inside the first enclosed floors of the building under consideration.

Each mean should be based on a large number of instantaneous signal strength samples

collected while moving over a distance of approximately 40 wavelengths. In cases where

room and hallway sizes may preclude such linear movement, an S- or U- shapedpattern of movement can be used. To mitigate against measurement errors due to

saturation of the signal strength detector or signal fading below the detector noise floor,

the median level of all instantaneous signal strengths is suggested as the value to

represent each local mean. The process is then repeated to obtain a number of local mean

values around the outside perimeter of the building at ground level. The difference

between the decibel-averaged inside median values and the decibel-averaged outside

median values is then taken as the mean building penetration loss for the building under

consideration.

When data for several buildings of the same type are available, the mean penetration loss

for that class of buildings can be taken as the decibel average of the individual building

penetration losses. However, building loss decreases with increasing frequency, at least

up to 3 GHz [3] (Hess, 1998: 181-182). The path loss dBL includes the value of the

clutter loss )(vL and is expressed as


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wwffdB anandvLL +++= 10log20)(

(2.26)Where fa is the attenuation in dB of the floors and wa is the attenuation in dB of the

walls andf

n andw

n are the number of floors and walls along the line d respectively. The

results vary from building to building depending on the type of construction of building,

the furniture and equipment it houses, and the number and deployment of people who

populate it [7] (Freeman, 1989: 891).

2.4.4.4The Refraction Model

The atmosphere has a profound effect on signal propagation. At frequencies above

30MHz, there are three effects worthy of mention [4] (Parsons, 2000: 26).

Localized fluctuations in refractive index, which can cause scattering

Abrupt changes in refractive index as a function of height, which can cause

reflection

A more complicated phenomenon known as ducting.

Variations in the climatic conditions within the atmosphere cause changes in the

refractive index of the air. Large-scale changes of refractive index with height cause radio

waves to be refracted, and at low elevation angles the effect can be quite significant at all

frequencies. Refraction has greatest effect on VHF and UHF point-to-point systems and

is therefore worth discussing. Ideally, the dielectric constant of atmosphere is unity and

there is zero absorption. In practice, the dielectric constant of air is greater than unity and

depends on the pressure and temperature of the air and the water vapour. It therefore

varies with weather condition and with height above the ground. A change in the


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atmospheric dielectric constant with height implies that electromagnetic waves are bent

in a curved path that keeps them nearer to the earth than would be the case if they truly

travelled in a straight line. In a standard exponential atmosphere, it can be shown that the

radius of curvature is given by

=

dn

dhP

(2.27)

Where h is the antenna height and n is the atmospheric index. The distance d, from an

antenna of height h to the optical horizon can be obtained. The maximum LoS range dis

given by the following formula [4] (Parsons, 2000: 29).

hrhrhrrhd 22)( 2222 +=+= (2.28)

so that hrd 2 when h


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greater than this critical rate, and is sufficient to cause the rays to be refracted back to the

surface of the earth. These rays are then reflected and refracted back again in such a

manner that the field is trapped or guided in a thin layer of the atmosphere close to the

earths surface. This phenomenon is known as trapping or ducting. The waves will then

propagate over a long distance with much less attenuation than for free-space propagation

[4] (Parsons, 2000: 27-30).

2.4.4.5The Fading Model

Substantial variations occur in the signal amplitude during propagation. The signal

fluctuations are known as fading.Short-term fluctuations are known as fast fading and

the long-term fluctuations are known as slow fading. Of the two, slow fading is of

profound effect. Mobile terminals moving into the shadow of hills or buildings cause

slow fading with the variations in signal strength and hence, slow fading is often referred

to as shadowing. The mean path loss due to slow fading closely fits a log-normal

distribution with a standard deviation that depends on the frequency and environment.

Thus, the term log-normal fading is also used [4] (Parsons, 2000: 114-116).

The simple path-loss model given in equation 2.30 is generally used. The exponent is a

parameter that needs to be determined from measurement data. The terms wm and kare

defined as the mean powers at distances dand 0d respectively.

= )( 0ddkmw

(2.30)


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Where is the path loss coefficient. wm and kexpressed in dB are given as follows [41]

(Gibson, 1997: par. 21.7.1).

ww mM 10log10=

(2.31)

kK 10log10=

(2.32)

Using the above expressions, wm can be expressed as follows.

)(log10 010 ddKMw =

(2.33)

The received signal power with the combined effect of path loss and shadowing in dB is

given by the following expression.

dBw ddKM += )(log10 010

(2.34)

Where is the correction factor for log-normal shadowing? The above expression

defines the log-normal shadowing path loss model. Measurement supports the log-normal

distribution for [42] (Goldsmith, 2004) as follows.

]2)(exp[]21[)( 22dBdBdB dBdB

P =

(2.35)


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Where )( dBP is the log-normal distribution,2

dB is the variance and

dB is the mean of

the log-normal shadowing?

2.5. Conclusion

The wireless channel has been well discussed in this chapter to set the precedence for

both propagation mechanisms and models. The propagation mechanisms have been

elaborated well to address the signal impairment factors during signal propagation. The

propagation models ranging from simple ones for the line-of-sight scenario to complex

ones for non line-of-sight scenario have been illustrated to facilitate the quantification of

the signal impairment factors and hence be able to devise a mechanism to mitigate them.

From this chapter, it is clearly shown that radio signal propagating from the transmitter to

the receiver encounters impairments. However, it remains a subject of discussion as to

what extent these impairments are accounted for in radio planning. Most of the models

discussed here are accounted for just by providing an overall multiplying factor to the

propagation loss estimation algorithms as will be shown in chapter three. However, this

does not account fully as to what extent each of the models affect a propagating radio

signal. Thus, it is important that each impairment factor be examined separately and its

effects to the propagating radio signal accounted for independently to be able to

approximate real propagation environment. This inefficiency in accounting for

propagation losses stands out as the main objective of this project.


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CHAPTER 3: PREDICTION MODELS AND TUNING

APPROACHES

3.1. Introduction

The basic elements of propagation path loss models were described in chapter 2. The

application of each model varies according to frequency, link range, terrain type, land

use/land cover, etc. As an indication of the manner in which transmission loss calculation

may be made, an examination of the more notable irregular terrain prediction models as

well as clutter prediction models is given in the following section.

3.2. Propagation Prediction Models

A radio propagation prediction model is a set of mathematical expressions, diagrams and

algorithms used to represent the radio characteristics of a given environment [37] (Neskovic,

Neskovic and Paunovic, 2002). A number of approaches have been developed to predict

coverage that makes use of propagation path loss models. While all these models try to

approximate signal strength at a particular receiving point or in a specific local area referred

to as a sector, the methods used generally vary in their approach and accuracy. In general,

propagation models can be either empirical (referred to as statistical) or theoretical (referred

to as deterministic), or a combination of these two (also called semi-empirical) [39] (Mishra,

2004: 93). On the basis of the radio environment to be studied, the radio propagation models

can be classified into two main categories, outdoor and indoor propagation models. Further,

in respect of the size of coverage area, the outdoor propagation models are subdivided into

two additional classes, macro-cell and micro-cell propagation models [37] (Neskovic,


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Neskovic and Paunovic, 2002). The following sections consider various propagation

models from these categories.

3.2.1 Okumura-Hata Model

The Okumura-Hata model [23] (Hata, 1980: 317325) or a variation of it is used by most

of the propagation tools. The model is based on an empirical relation derived from

Okumuras report on signal strength and variability measurements [24] (Okumura et al,

1968: 825-873). The model is parameterized for various environments, namely urban,

suburban and open areas. It is applicable to:

Frequencyf(150...1500 MHz)

Distance between transmitter and receiver d(1...20 km)

Antenna height of the transmitter h t(30...200 m)

Antenna height of the receiver h r(1...10 m)

Since the model only requires four parameters for the computation of path loss, the

computation time is very short. This is the primary advantage of the model. However, the

model neglects the terrain profile between transmitter and receiver, i.e. hills or other

obstacles between the transmitter and the receiver are not considered. However, Hata and

Okumura made the assumption that the transmitter would normally be located on hills

and could ignore basic terrain losses. Also, phenomena such as reflection and shadowing

are not included in the model [43] (AWE Communications, S.a.).

Since the height of the transmitter and the receiver is measured relative to the ground, an


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effective antenna height heff is additionally used and added to the antenna height of the

transmitter to improve the accuracy of the prediction. The parameters marked green in the

figure 3.1 are the parameters considered by the Okumura- Hata model.

Figure 3.1:Definitions of Factors Neglected in Okumura- Hata Model.

In this example, the prediction would be too optimistic since the model assumes line-of-

sight trans

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