Application of propagation models to compute wireless radio coverage maps to realistic terrain data

For comparison purpose, Hata-Okumura with Knife Edge Diffraction Extension Method Hata-Okumura with Extension in short is used as reference method.. Propagation models are developed to c

DUBLIN CITY UNIVERSITY SCHOOL OF ELECTRONIC ENGINEERING

APPLICATION OF PROPAGATION MODELS

TO COMPUTE WIRELESS RADIO COVERAGE

MAPS TO REALISTIC TERRAIN DATA

Trinh Xuan Dung

August 2009

MASTER OF ENGINEERING

IN TELECOMMUNICATIONS

Supervised by: Dr Conor Brennan

Acknowledgements

I would like to give my special thanks to my supervisor Dr Conor Brennan for his guidance, enthusiasm and commitment to this project

To Prof Charles McCorkell, I say thanks for his help during the time I have been in Dublin

Thank finally to my family, Ngoc Dung, and friends who give me encouragement, inspiration to finish this project

Declaration

I hereby declare that, except where otherwise indicated, this document is entirely my own work and

has not been submitted in whole or in part to any other university

Signed: Date:

ABSTRACT

Integral Equation (IE) method is a deterministic method to compute the UHF terrain loss This method has an extremely huge computation load In this project, we attempt to implement Fast Far Field Approximation (FAFFA) This is a fast solution for IE method We then evaluate the efficiency of this method in the terms of accuracy and calculation time For comparison purpose, Hata-Okumura with Knife Edge Diffraction Extension Method (Hata-Okumura with Extension in short) is used as reference method Finally, we attempt to implement the FAFFA on real map (2 dimension map)

path-Table of Contents

Acknowledgements ii

Declaration ii

ABSTRACT iii

TABLE OF FIGURES vi

TABLE OF TABLES viii

CHAPTER 1: INTRODUCTION 1

CHAPTER 2: TECHNICAL BACKGROUND 3

2.1 Basic Propagation Model 3

2.2.1 Radio Wave Propagation 3

2.2.2 Free space Propagation Model 4

2.2.3 Hata-Okumura Model 5

2.2 Diffraction Theory 7

2.2.1 Diffraction basics 7

2.2.2 Single Knife Edge Diffraction 11

2.2.3 Multiple knife edges diffraction 12

2.3 Deterministic Method 15

2.3.1 Integral Equation 15

2.3.2 Fast Far Field Approximation (FAFFA) 20

CHAPTER 3: IMPLEMENTATION 23

3.1 1 Dimension Implementation 23

3.1.1 Epstein Peterson Diffraction 23

3.1.2 Integral Equation Method 28

3.1.3 Fast Far Field Approximation 30

3.2 2 Dimension Implementation 32

3.2.1 Find path Algorithm 32

3.2.2 Path Linearization 35

3.2.3 Improvement of FAFFA in the 2D computation 37

CHAPTER 4: RESULTS 38

4.1 1 Dimension MAP 38

4.1.1 FAFFA and Integral Equation method 38

4.1.2 FAFFA and Hata-Okumura with Epstein Peterson KED Extension 40

4.2 2 Dimension MAP 42

CHAPTER 5: CONCLUSION AND FURTHER WORK 44

5.1 Conclusion 44

5.2 Further Work 44

REPERENCES 45

APPENDIX 46

A1 CD Structure 46

A2 More results over rural terrain 47

TABLE OF FIGURES

Figure 1-1: Graphical Interface of coverage map computation softwares 2

Figure 2-1: Small scale and Large scale propagation model 3

Figure 2-2: Path-loss against distance using free space propagation model 5

Figure 2-3: Empirical model: the dots are the measurements 5

Figure 2-4: Simple straight-ahead model 7

Figure 2-5: A shape of a radio shadow 7

Figure 2-6: Huygens’ wavelets filling a radio shadow 8

Figure 2-7: Vector addition of contributions from a wavefront 9

Figure 2-8: Cornu spiral 9

Figure 2-9: Cornu Spiral in case an obstruction exists 10

Figure 2-10: Fresnel-Kirchoff knife-edge diffraction curve 10

Figure 2-11: Knife edge diffraction parameter 11

Figure 2-12: F(v) vs J(v) 12

Figure 2-13: Epstein Peterson Construction 13

Figure 2-14: Epstein Peterson Construction 14

Figure 2-15: Bullington Construction 14

Figure 2-16: Geometry of the transmission from a source to a receiver 15

Figure 2-17: Piecewise function 18

Figure 2-18: Forward scattering and backward scattering 19

Figure 2-19: Illustration idea of FAFFA Algorithm 20

Figure 2-20: FAFFA Algorithm 22

Figure 3-1: Different types of potential obstructions 24

Figure 3-2: All potential obstructions between the transmitter and receiver 24

Figure 3-3: Obstructions of the path (a) Fake Obstruction (b) Real Obstruction 25

Figure 3-4: Detection of real or fake obstruction 25

Figure 3-5: Usage of “long jump” to increase the speed of algorithm 26

Figure 3-6: Real Obstructions 26

Figure 3-7: Knife edge diffraction parameter 27

Figure 3-8: Total obstruction loss of Hjorring, Danmark 27

Figure 3-9: Real map and interpolated map 28

Figure 3-10: Points grouping 30

Figure 3-11: Demonstration of Tij calculation 31

Figure 3-12: Illustration of computing scattered field from far field points 31

Figure 3-13: Illustration of find path algorithm (a) step 1 (b) step 2 32

Figure 3-14: Result of find path algorithm 33

Figure 3-15: Division of map into 4 corner 34

Figure 3-16: Result of the find path algorithm when it applies to real map 34

Figure 3-17: Non-linearity of the path- side view 35

Figure 3-18: Non-linearity of the path- top view 35

Figure 3-19: Linearization of the path 36

Figure 3-20: Result of linearization 36

Figure 3-21: Fast Implementation of FAFFA on 2D Map 37

Figure 4-1: Comparison of IE Method and FAFFA Method 38

Figure 4-2: Comparison of FAFFA Method, Hata Okumura with Epstein Peterson KED Extension 40

Figure 4-3: Comparison of FAFFA Method, Hata Okumura with Epstein Peterson KED Extension 41

Figure 4-4: Fast Implementation of FAFFA on 2 Dimension Map 42

Figure 4-5: Result of Fast Implementation of FAFFA on 2D Map 42

Figure 4-6: A suggestion to reduce error of “enhanced method” 43

Figure 4-7: Another suggestion to reduce error of “enhanced method” 43

TABLE OF TABLES

Table 4-1: Comparison of different methods Scenario: Hjorring, Denmark 39 Table 4-2: Comparison of different methods Scenario: Hadsund, Denmark 39

CHAPTER 1: INTRODUCTION

The past few years have witnessed a dramatically growth in the wireless industry, both in terms of mobile technology and numbers of subscribers Mobile networks have been being more important in the human life A disadvantage of wireless networks is that the mobile station needs to

be in the coverage area to communicate to the network The coverage area of a base station is the geographic area where the base station can communicate If a mobile station is out the coverage area, he cannot communicate with the network Therefore, calculation of coverage map is essential

in designing a mobile network

If the received power at a mobile station is below a certain threshold, that subscriber cannot communicate with the network Hence, we must know the received power of all points in the map

to compute the coverage map The problem of coverage map calculation turns into problem of received field computation Propagation models are developed to compute the received field at a certain distance from the transmitter Each of propagation models can only be applied in a particular scenario:

- Rural area: Propagation of electromagnetic waves in areas with a low density of buildings depends mainly on the topography and the land usage (clutter) For rural area, two rays model, Hata-Okumura model, Parabolic Equation model, Hata-Okumura with Knife Edge Diffraction are usually used to compute the received field

- Urban area: Propagation of electromagnetic waves in urban scenarios is influenced by reflections and diffractions at the buildings Typically the multipath propagation is very important

in urban environments Therefore, several empirical model like Hata-Okumura Model, Walfish Ikegami Model and Ray Optical Propagation model are offered to compute the received field

- Indoor area: Propagation of electromagnetic waves inside buildings is influenced mainly

by the walls Phenomena like multi-path propagation, reflection, diffraction and shadowing have a significant influence on the received power So the propagation models should consider these phenomena to obtain accurate results Simple empirical propagation models are therefore not sufficient In this situation, One Slope Model, Montley Keenan Model, COST 231 Multi Wall Model are usually applied to compute received field [13]

- Tunnel: The propagation of electromagnetic waves in tunnels differs significantly from the propagation in outdoor environments Therefore new approaches to the modeling of the scenario are required to obtain accurate prediction results [12] The following propagation models can be used: One Slope Model, Montley Keenan Model, COST 231 Multi Wall Model

- Other scenarios: satellite communication, vehicles, combined scenarios, etc

In this project, we have focused on investigating the propagation models for rural area

Some softwares have been developed to calculate the coverage map Two of the most popular products are designed by AWE Communication [13] and Akosim [14] Figure 1-1a and 1-1b show graphical interfaces of ProMan (product of AWE Communication) and FUN (product of Akosim)

Figure 1-1: Graphical Interface of coverage map computation softwares

(a) FUN (b) ProMan

For rural area, Hata-Okumura with Knife Edge Diffraction Extension is the most popular propagation model It is widely used in most of propagation softwares In this project, I try to suggest another model: Fast Far Field Approximation (FAFFA) to compute the received field We evaluate the efficiency of Hata-Okumura with Extension and FAFFA in terms of accuracy and computation load We then find out the advantages and disadvantages of FAFFA compared to Hata-Okumura with Extension Model Finally, we implement the FAFFA on a real map (2 dimension map)

CHAPTER 2: TECHNICAL BACKGROUND

In this chapter, we investigate technical backgrounds which are related to the project We first investigate several popular propagation models including free space model and Hata-Okumura Model Hata-Okumura Model is chosen as the reference method to estimate the efficiency of Fast Far Field Approximation However, Hata-Okumura is not accuracy in the shadowed zones In order

to improve the accuracy of Hata-Okumura Model in the shadowed zones, we need to extend this method with the Knife Edge Diffraction Theory We then study the Integral Equation method and finally study the fast solution for the Integral Equation method, Fast Far Field Approximation (FAFFA)

2.1 Basic Propagation Model

2.2.1 Radio Wave Propagation

The mechanisms of electromagnetic wave propagation are diverse, but can be attributed into reflection, diffraction and scattering Most cellular systems operate in the areas that there is no line

of sight between the transmitter and receiver, and the presence of obstructions like hills, buildings will cause the severe diffraction loss The propagation models are used to estimate the received signal strength at a certain distance between the transmitter and receiver

Traditionally, propagation models have focused on predicting the average received signal strength at a distance between the transmitter and receiver Propagation models that predict the

mean signal strength for a distance are called large-scale propagation models On the other hand,

propagation models that characterize the rapid fluctuations of the received signal strength over very short travel distances (a few wavelengths) or short time durations (a few seconds) are called small-scale propagation models [11] Figure 2.1 illustrates the differences between the large-scale propagation models and small-scale propagation models In the figure, signal fades rapidly as the receiver moves, but the local average signal strength changes much more slowly with distance

Figure 2-1: Small scale and Large scale propagation model

-60 -50 -40 -30 -20 -10 0 10

Large-scale propagation models characterize signal over large transmitter-receiver distance Therefore, they are useful in estimating the radio coverage area In this project, we only focus on calculating the radio coverage area Hence, we concentrate on the large-scale propagation models

2.2.2 Free space Propagation Model

This is the simplest propagation model It is used to predict the signal received strength when the transmitter and receiver have a clear, un-obstructed line-of-sight between them The free space power received by a receiver is given by the Friis free space equation: [8]

  

Where: PT is the transmitted power, PR(d) is the received power

GT is the transmitter antenna gain, GR is the receiver antenna gain

d is the distance between the transmitter and reciver

L is the system loss factor It is not related to the propagation

λ is the wavelength in meters

The path-loss, which represents signal attenuation, is defined as the difference between the effective transmitter power and receiver power The path-loss of free space model is given by:

  

   

   (2-2) When antenna gain is excluded, the antennas are assumed to have unity gain, the path-loss becomes:

  

     (2-3)

Figure 2.2 is an illustration of the Path-loss of free space propagation model against distances between the transmitter and receiver We can recognize that the received power decays with distance at a rate of 20dB/decade

Figure 2-2: Path-loss against distance using free space propagation model 2.2.3 Hata-Okumura Model

Free space model is rather simple In real situation, propagation loss is more complicated The received field is a combination of directed field, diffracted field and scattered field This make the computation of received field be complex And empirical models are used to reduce the complexity of computation Hata-Okumura is an empirical model To create an empirical model, a set of actual path loss measurement is made and an appropriate function is fitted to the measurements

Figure 2-3 : Empirical model: the dots are the measurements and the line represents the best fit empirical model

An example of an empirical model fitted to measurements is shown in figure 2.3 The simplest useful form for an empirical path loss models is:



-100 -90 -80 -70 -60 -50 -40

Figure: Free Space Loss

Or in decibels:    ! " #

Where: PT and PR are the transmitted and predicted received power

L is path-loss and r is distance between transmitter and receiver

K = 10log10(k) and n are the constant of the model

Hata-Okumura model is a fully empirical prediction method, based entirely upon an extensive series of measurements made in around Tokyo city between 200 MHz and 2 GHz Predictions are made via a series of graphs and the most important graphs have been approximated

in set of formulae by Hata [3]

This method divides the prediction area into a series of categories, named open, suburban and urban:

- Open area: Open space, no tall trees or building in path, plot of land cleared for 300-400 m ahead Example: rice fields, farmland,

- Sub-urban area: village or highway scattered with trees and houses

- Urban area: city or large town with large building and houses

Okumura’s predictions of median path loss are usually calculated using Hata’s approximations as follows: [3]

E = 3.2(log(11.75hm))2 – 4.97 for large cities, fc ≥ 300 MHz

E = 8.29(log(1.54hm))2 – 4.97 for large cities, fc< 300 MHz

E = (1.1logfc – 0.7)hm – (1.56logfc – 0.8) for medium to small cities

The model is valid only for 150 MHz ≤ fc ≤ 1500 MHz, 30 m ≤ hb ≤ 200 m, 1 m ≤ hm ≤ 10m and R > 1 km

Base station antenna height hb is defined as the height above the average ground level in the range 3-10 km from the base station

Urban areas are divided into large cities and medium/small cities where an area having the average building height in excess of 15 m is defined as a large city

The Hata-Okumura model, together with the corrections is probably the single most common model used in designing real systems

2.2 Diffraction Theory

2.2.1 Diffraction basics

Diffraction is the “bending” of the wavefronts around obstacles Diffraction occurs with all propagating waves, including sound waves, wave on water, wave in materials and electromagnetic waves With visible light, It is normally too small to see With radio waves, it occurs at much larger scales

The simple view of radio wave is that it travels in straight lines The “ray” is a familiar concept This is the concept of an expanding wavefront, which suggests power radiating straight outwards Figure 2.4 illustrates how a plane wavefront would advance if the expanding wavefront is applied for the propagation [9]

Figure 2-4: Simple straight-ahead model

In figure 2.5, a radio wave propagates from the left to the obstruction On the right of

Figure 2-5: A shape of a radio shadow

the obstruction, the resulting radio energy is represented by a graph with the radio wave power plotted against the height Without the obstruction, the power of the waves has the form of the dotted line With the presence of obstruction, the power changes to the solid line

The first reason for this shape is that the radio energy does not simply travel in a ahead manner Christiaan Huygens, a Danish scientist, is the first person who devises a method for

straight-finding the subsequent positions of an advancing wavefront His rule is “Each point on a wavefront

acts as a source of secondary wavelets The combination of these secondary wavelets produces the new wavefront in the direction of propagation” [15] This is illustrated in figure 2.6-a, we have a

plane wavefront advancing from the left When the wavefront comes the obstruction as illustrated

in figure 2.6-b, some new wavelets come to the shadow zone and form a new wavefront

This shows that energy can leak into a shadow The exactly calculation of diffraction is complicated, but a qualitative can be derived from a construction know as the Cornu Spiral

Figure 2-6: Huygens’ wavelets filling a radio shadow

Figure 2.7 shows a wavefront which has reached the line A-A’ We want to consider the resulting signal level at point B We take a set of rays from uniformly spaced points along the wavefront and allow them to converge at B Radio signals form vector fields Hence, to compute field at B, we must add these fields by graphical method as showed in figure 2.7

Figure 2-7: Vector addition of contributions from a wavefront

In figure 2.7, we have stopped at a total five arrows If the process of adding contributions is continued indefinitely, the sequences of arrows above and below “arrow 0” will form two spirals, which is called Cornu spiral

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Figure 2-8: Cornu spiral

The complete double spiral represent the free-space propagation, with no obstruction arriving at B from anywhere of the advancing wavefronts The result is a vector from X to Y

Now we put a obstruction between AA’ and B as illustrated in figure 2.9 This obstruction will cut off the lower part of the spiral This will give the ripples above the obstruction

-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

Figure 2-9: Cornu Spiral in case an obstruction exists

Figure 2.10 shows the actual variation of illumination for knife-edge diffraction loss It is known as the Fresnel-Kirchoff curve, and it is plotted as diffraction loss against a geometer parameter υ

Figure 2-10: Fresnel-Kirchoff knife-edge diffraction curve

Figure 2.8, 2.9 and 2.10 are sketched by Matlab script: Cornu_Spriral.m

2.2.2 Single Knife Edge Diffraction

The propagation loss, which expresses the reduction in the field strength due to diffraction, can be computed in decibels as: [9]

$  % &'()

( *'+  %,-., (2-6) Where Ed is the diffracted field, Et is the incident field, and:

-. /01 2 3∞ 45678 9

An alternative form is:

,-., 141" <1.  <. " =1.  =.9 (2-8) Where <. and =. are the Fresnel cosine and sine integrals

The parameter can be expressed in terms of the geometry parameters defined in figure 2.11 as:

Figure 2-11: Knife edge diffraction parameter

A useful approximation to knife edge diffraction loss in dB for greater than about -0.7 is:

D.  EFG " %@H.1"  " B (2-10) This equation is simplified form of the expression given in Recommendation 526-4 [12]

The most widely used approach to predict multiple knife edge diffraction is to use an approximation method which simplifies the path geometry to calculate the total diffraction loss in terms of combinations of single edge diffractions between adjacent edges Some of approximation methods are: Epstein-Peterson, Deygout, Bullington,

2.2.3.1 Epstein Peterson Approximation [4]

The method of Epstein Peterson is illustrated in figure 2.13, in which 3 hill tops are represented by knife edges at A, B and C For each edge, the diffraction loss is computed as through

a diffraction path exists in isolation with the neighboring points [6] Thus is computed for TAB, ABC and BCR

Figure 2-13: Epstein Peterson Construction

Hence, the parameter for each of the edges is first calculated:

.  I 1I >JI >KI >L (2-11a) 1  1I MI >KI >LI >N (2-11b) M  1I MI >LI >NI > (2-11c) Then, the total loss is then computed by combining the loss from all edges:

Figure 2-14: Epstein Peterson Construction

The main edge splits the path into two sub-paths The v parameters are then calculated for the two segments with new paths:

.′  I 1I >JI >KI >L (2-15a) M′  MI I >LI >NI > (2-15b) The total loss is then calculated by combining the loss of main edge and from each of the two sub-paths:

J  $@.′B " $.1 " $@.M′B (2-16)

2.2.3.3 Bullington Approximation [5]

The Burlington method is quite simple It is based on constructing an equivalent single knife edge at the intersection of transmitter and receiver Then we calculate the loss based on that edge

Figure 2-15: Bullington Construction

This method is easy to do but is prone to underestimate loss as it can ignore important

intermediate edges

2.3 Deterministic Method

2.3.1 Integral Equation

2.3.1.1 Integral Equation Basics

Integral Equation (IE) method is a technique used to find the solution for induced current in the form of integral equation where the unknown induced current is part of the integrand Then, numerical techniques can be used to solve for the current density Once this is accomplished, the scattered fields can be found using the radiation integrals [7]

The field radiated by a line source of constant current Iz (referred as directed field Ed) is given by:

STU  V ... grouped into groups, each of them has a center Using equalized size groups of M points will lead

us to N/M groups where N is the total points of the path Figure 2-19 show an illustration of FAFFA... is to use an approximation method which simplifies the path geometry to calculate the total diffraction loss in terms of combinations of single edge diffractions between adjacent edges Some of. .. class="page_container" data- page="17">

Figure 2-7: Vector addition of contributions from a wavefront

In figure 2.7, we have stopped at a total five arrows If the process of adding contributions