Upload
hiew-kf
View
219
Download
0
Embed Size (px)
Citation preview
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
1/170
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
2/170
Page 1
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
3/170
Page 2
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
4/170
Page 3
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
5/170
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
6/170
Page 5
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
7/170
Page 6
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
8/170
Page 7
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
9/170
Page 8
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
10/170
Page 9
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
11/170
Page 10
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
12/170
Page 11
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
13/170
Page 12
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
14/170
Page 13
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 6.14: T0877- Predicted Versus Measured Signal. .............................................. 138
Figure 6.15: T0877-Error between Measured and predicted signal. ............................ 138
Figure 6.16: T0894- Predicted Versus Measured Signal. .............................................. 139
Figure 6.17: T0894-Error between Measured and predicted signal. ............................ 140
Figure 6.18: T4658- Predicted Versus Measured Signal. .............................................. 141Figure 6.19: T4658-Error between Measured and predicted signal. ............................ 141
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
15/170
Page 14
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
16/170
Page 15
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
17/170
Page 16
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
18/170
Page 17
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
19/170
Page 18
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
20/170
Page 19
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
21/170
Page 20
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
22/170
Page 21
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
23/170
Page 22
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
24/170
Page 23
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
25/170
Page 24
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
26/170
Page 25
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]
(Aguiar and Gross, 2003).
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
27/170
Page 26
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]
(Aguiar and Gross, 2003).
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
28/170
Page 27
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;
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
29/170
Page 28
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
30/170
Page 29
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
31/170
Page 30
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
32/170
Page 31
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
33/170
Page 32
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
34/170
Page 33
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
35/170
Page 34
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
36/170
Page 35
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
37/170
Page 36
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
38/170
Page 37
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
39/170
Page 38
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
40/170
Page 39
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
41/170
Page 40
[ ]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).
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
42/170
Page 41
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)
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
43/170
Page 42
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 +=
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
44/170
Page 43
)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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
45/170
Page 44
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
46/170
Page 45
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)
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
47/170
Page 46
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
48/170
Page 47
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
49/170
Page 48
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
50/170
Page 49
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
51/170
Page 50
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).
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
52/170
Page 51
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)
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
53/170
Page 52
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
54/170
Page 53
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
55/170
Page 54
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
56/170
Page 55
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
57/170
Page 56
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)
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
58/170
Page 57
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)
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
59/170
Page 58
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.
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
60/170
Page 59
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,
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
61/170
Page 60
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
8/3/2019 Http Libserv5.Tut.ac.Za 7780 Pls Eres Wpg Docload.download File p Filename=F587524610 MukubwaWE
62/170
Page 61
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