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5.3. Comparison & Field Data Collection During Different Climate
133
Conditions
5.4. Development of Propagation Path Loss Model By Considering
139
Different Climatic Conditions
5.4.1. Effect
of
Summer
139
5.4.2. Effect
of
Winter
140
5.4.3. Effect
of
Rain
140
5.4.4. Effect
of
Fog
142
5.5. Comparative Analysis of Field Measured Data, Okumura Model and
143
Developed Okumura Model
5.6. Validation of Developed Okumura Path Loss Model
149
5.6.1. By Taking Reference Model
151
5.6.1.1. Fog Attenuation Reference Model
151
5.6.1.2 . Rain Attenuation Reference Model
152
5.6.2. By Applying The Developed Model in Another City
156
5.7
. Conclusion
161
6. Cell Coverage Area And Effect On Link Budget Due To Climatic
163
Conditions
6.1.
Introduction
163
6.2.
Coverage
Area
164
6.3.
Link
Budget
and
Its
Calculations
166
6.3.1. Important Parameters of Link Budget Calculations
169
6.3.1.1 Receiver
Sensitivity
169
6.3.1.2 MS
Sensitivity
169
6.3.1.3
BTS
Sensitivity
169
6.3.1.4 MS & BTS Antenna Gain
170
6.3.1.5 Diversity
Gains
170
6.3.1.6 Feeder & Connector Loss
170
6.3.1.7 Pre Amplifier & Booster
170
6.3.1.8 Interference Degradation Margin
171
6.3.1.9 Polarization
Loss 171
6.3.2. Uplink Budget And Coverage Area
172
6.3.2.1. Transmitting End
172
6.3.2.2.
Receiving
End
173
6.3.3.Down Link Budget And Coverage Area
174
6.3.3.1
Transmitting
End 174
6.3.3.2.
Receiving
End
175
6.4. Effect of Climatic Conditions on Link Budget
177
6.4.1. Calculation of Link Budget & Coverage Area in Summer and
177
Winter
6.4.2. Calculation of Link Budget and Coverage Area in Heavy Fog
179
(visibility=30m)
6.4.3. Calculation of Link Budget and Coverage Area in Heavy Rain
181
(100mm/hr)
6.4.4. Calculation of Link Budget and Coverage Area Including All
183
Climatic Effects in Narnaul (Haryana, India).
6.5. Conclusion
185
7. Conclusion
And
Future
Work
187
7.1. Results
&
Conclusion
187
7.2. Future
Work
190
References
191
Appendices
207
LIST OF FIGURES
Figure No.
Description Page
No.
1.1 Year
Wise
Development
of
Wireless
Communication
2
1.2
Global Growths of Mobile and Fixed Subscribers
3
1.3 Illustration
Showing
the
Importance
of
Accurate
Coverage Estimation in Cellular Networks as Compared
to Early Land to Mobile System
5
1.4
First Generation Cellular Phone of 1924
6
1.5
1.6
1.7
2.1
2.2
2.3
Concept of Frequency Reuse
Illustration of Frequency Reuse Concept
Basic of Handoff
The Hertzian Dipole
Voltage Induced at the Receiver Antenna
Illustration of Wireless Communication Showing Path
Loss
7
8
8
26
30
32
2.4
Phenomenon of Reflection and Refraction
34
2.5
Diffraction in Sharp Edge
35
2.6
Wave is Scattered by a Small Obstacle
35
2.7
Example of Free Space Communication
38
2.8
Median Attenuation Relative to Free Space A
mu
(f,d) Over
a Quasi-smooth Terrain
42
2.9 Correction
Factor
G
area
for Different Types of Terrain
42
3.1
User Interface of Nemo Drive Test Tool
50
3.2
3.3
3.4
3.5
3.6
E7478A Drive Test System with E6455C IMT2000
Digital Receiver (Agilent Data Collection Tool)
Pioneer Data Collection Tool
Window of XTEL's Data collection Tool
Test Principle Illustrations
TEMS Test Kit used for test drive
51
52
53
59
60
3.7 TEMS
window
64
3.8
Equipment Configuration Windows in TEMS
65
3.9
Equipment Configuration Window
66
3.10
3.11
3.12
3.13
3.14
Cell Data Configuring Window
Example of loading of Cell File in Narnaul (South
Haryana)
Five basic objects of Map info
Illustration of Map Layer
The Default MATLAB Desktop
67
68
81
82
83
vi
4.1
4.2
Selected Cell Sites for Field Data Collection
Derive Test Result in Cell id NNL001
86
87
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
4.17
4.18
4.19
4.20
4.21
4.22
4.23
4.24
4.25
Variation of Received Signal Strength (dBm) with
Distance (Km.) in Three Different Areas of Five Different
Cell ids
Variation of Path loss (dB) with Distance (Km.) in Three
Different Areas of Five Different Cell ids
Comparison of field measured path loss and Predicted
path loss with distance (Site id NNL001)
Comparison of field measured path loss and Predicted
path loss with distance (Site id NNL002)
Comparison of Field Measured Path Loss and Predicted
Path Loss With Distance (Site id NNL003)
M-file of Free Space Path Loss Model
Comparison Between Fields Measured Path Loss and Free
Space Path Loss Model
Variation of Path Loss Between Free Space Path Loss and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and Free
Space Path Loss Model
M-file of W-I Path Loss Model
Comparison Between Field Measured Path Loss and W-I
Path Loss Model
Variation of Path Loss Between W-I Path Loss and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and W-I
Path Loss Model
M-file of Lee path loss model
Comparison Between Field Measured Path Loss and Lee
path loss model
Variation of Path Loss Between Lee Path Loss and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and Lee
Path Loss Model
M-file of Egli Path Loss Model
Comparison Between Field Measured Path Loss and Egli
Path Loss Model
Variation of Path Loss Between Egli Path Loss and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and Egli
Path Loss Model
M-file of Bertoni Path Loss Model
Comparison Between Field Measured Path Loss and Bertoni
Path Loss Model
87
88
91
91
92
93
93
94
95
97
97
98
98
100
100
101
101
103
103
104
104
105
107
vii
4.26
4.27
4.28
4.29
4.30
4.31
4.32
4.33
4.34
4.35
4.36
4.37
4.38
4.39
4.40
4.41
4.42
4.43
4.44
4.45
4.46
4.47
5.1
5.2
5.3
5.4
Variation of Path Loss Between Bertoni Path Loss and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and
Bertoni Model
M-file of Okumura Path Loss Model
Comparison Between Field Measured Path Loss and
Okumura Path Loss Model
Variation of Path Loss Between Okumura Path Loss
Model and Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and
Okumura Path Loss Model
M-file of COST 231 path loss model
Comparison Between Field Measured Path Loss and
COST 231 Path Loss Model
Variation of Path Loss Between Cost 231 Model and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and Cost
231 Model
M-file of ECC 33 Path Loss Model
Comparison Between Field Measured Path Loss and
ECC-33 Path Loss Model
Variation of Path Loss Between ECC-33 Path Loss Model
and Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and
ECC-33 Path Loss Model
M-file of SUI Path Loss Model
Comparison Between Field Measured Path Loss and SUI
Path Loss Model
Variation of Path Loss Between SUI Path Loss Model and
Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and SUI
Path Loss Model
M-file of Hata Path Loss Model
Comparison Between Field Measured Path Loss and Hata
Path Loss Model
Variation of Path Loss Between Hata Path Loss Model
and Practical Field Data for Two Adjacent Cells
Variation of Error Between Field Measured Data and SUI
Path Loss Model
Geographical Map of Haryana
Average Rainy Days per Month in the Year 2011-2012
Average Rain Fall in Year 2011-2012
Satellite View & Climate (foggy day) of Narnaul
107
109
110
110
111
111
113
114
114
115
117
117
118
118
120
120
121
121
123
124
124
126
129
131
131
132
viii
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
5.16
5.17
5.18
5.19
5.20
5.21
5.22
5.23
5.24
Average Fog Hours per Day in Year 2011-2012
Variation of Path Loss in Different Climatic Conditions
Error between Measured Data and Okumura Model in
Winter
Error between Measured Data and Okumura Model in
Summer
Error between Measured Data and Okumura Model in
Heavy Fog Climate
Error between Measured Data and Okumura Model in
Heavy Rain Climate
Illustration of Collision of Atoms and Molecules
Effect of Sun in Frequency Spectrum
Effect of Rain on Radio Waves
Comparison Between Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Winter Climate
Comparison Between Approximated 4
th
Degree
Polynomial Curve of Field Measured data, Okumura and
Developed Okumura Path Loss Model in Winter Climate
Comparison Between Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Summer Climate
Comparison Between Approximated 4
th
Degree
Polynomial Curve of Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Summer Climate
Comparison Between Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Heavy Fog
Climate
Comparison Between Approximated 4
th
Degree
Polynomial Curve of Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Heavy Fog
Climate
Comparison Between Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Heavy Rain
Climate
Comparison Between Approximated 4
th
Degree
Polynomial Curve of Field Measured Data, Okumura and
Developed Okumura Path Loss Model in Heavy Rain
Climate
Variation of Error Between Field Measured Data,
Okumura and Developed Okumura Model in Winter
Variation of Error Between Field Measured
Data,Okumura and Developed Okumura Model in
Summer
Variation of Error Between Field Measured Data,
Okumura and Developed Okumura Model in Foggy
Climate
132
133
136
136
137
137
139
140
141
143
144
144
145
145
146
146
147
147
148
148
ix
5.25
5.26
5.27
5.28
5.29
5.30
5.31
5.32
5.33
5.34
5.35
5.36
5.37
6.1
6.2
6.3
6.4
6.5
6.6
Variation of Error Between Field Measured Data,
Okumura and Developed Okumura Model in Winter
Comparison Between Developed Fog Attenuation and
Reference Fog Attenuation Model
Difference Between Developed and Reference Fog
Attenuation Model
Comparison Between Developed Rain Attenuation and
Reference Rain Attenuation Model
Difference Between Developed and Reference Rain
Attenuation
Comparison Between Developed Okumura Model and
Field Data Taken (Hisar, Haryana, INDIA) in winter
season
Comparison Between Developed Okumura Model and
Field Data Taken (Hisar, Haryana, INDIA) in summer
season
Comparison between Developed Okumura Model and
Field Data Taken (Hisar, Haryana, INDIA) in Heavy Fog
Condition
Comparison between Developed Okumura Model and
Field Data taken (Hisar, Haryana, INDIA) in Heavy Rain
Condition
Error between Developed Okumura Model and Field Data
taken (Hisar, Haryana, INDIA) in Winter Condition
Error between Developed Okumura Model and Field Data
taken (Hisar, Haryana, INDIA) in Summer Season
Error between Developed oOkumura Model and Field
Data taken (Hisar, Haryana, INDIA) in Heavy Fog
Condition
Comparison between Developed Okumura Model and
Field Data taken (Hisar, Haryana, INDIA) in Heavy Rain
Condition
Illustration of Link Budget in Mobile Communication
Methodology Used for Prediction of Optimum Coverage
Area
Uplink & Downlink Budget
Mast Head Amplifier
Uplink Budget & Flow Chart (Uplink Budget)
Downlink Budget & Flow Chart (Downlink Budget)
149
151
152
153
153
156
156
157
157
158
158
159
159
164
166
168
171
173
174
x
LIST OF TABLES
Table No.
Description Page
No.
1.1
2.1
Evolution of The WLAN Standards
The Parameter Values of Different Terrain for SUI Model
4
46
3.1
Range of SQI
58
3.2
3.3
Some TEMS Supported Mobile Phone's Feature
Signal Strength Measurements at Base Station NNL001in Month
of January (Winter)
61
70
3.4
Signal Strength Measurements at Base Station NNL011 in Month
of May (Summer Temperature:47
0
C)
71
3.5
Signal Strength Measurements at Base Station NNL001 in Month
of July (Heavy Rain)
72
3.6
Signal Strength Measurements at Base Station NNL001 in Month
of December (Winter Heavy Fog Condition)
73
3.7
3.8
3.9
3.10
3.11
3.12
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
4.12
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
Signal Strength Measurements at Base Station Hisar in Month of
January
Signal Strength Measurements at Base Station Hisar in Month of
May
Signal Strength Measurements at Base Station Hisar in Month of
July
Signal Strength Measurements at Base Station Hisar in Month of
December
Main Parts of MATLAB
Different Parts of MATLAB Window
Average Signal Strength Measurements
Average Path Loss Measurements
Error Between Measured and Free Space Path Loss Model
Error Between Measured and W-I Path Loss Model
Error Between Measured and Lee Path Loss Model
Error Between Measured and Egli Path Loss Model
Error Between Measured and Bertoni Path Loss Model
Error Between Measured Okumura Path Loss Model
Error Between Measured and Cost 231 Path Loss Model
Error Between Measured and ECC-33 Path Loss Model
Error Between Measured and SUI Path Loss Model
Error Between Measured and Hata Path Loss Model
Different Seasons of India
Various Climatic Regions of India
Rainfall Statistics for Haryana
Variation of Maximum and Minimum Temperature in Haryana
Average Signal Strength Measurements at Narnaul (Haryana)
Average Path Loss Measurements at Narnaul (Haryana)
Error Between Okumura Path Loss Model and Field Data
Error Between Measured, Okumura and Developed Okumura
74
75
76
77
83
84
89
90
96
99
102
106
108
112
116
119
122
125
128
129
130
130
134
135
138
150
Model
xi
5.9
5.10
5.11
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
7.1
7.2
7.3
Error Between Fog Attenuation and Reference Model
Error Between Rain Attenuation and Reference Model
Error Between Measured Data (Hisar, Haryana, India) and
Developed Okumura Model
Transmitter Side Specifications (Uplink)
Receiver Side Specifications (Uplink)
Transmitter Side Specifications (Down Link)
Receiver Side Specifications (Down Link)
Coverage Area Calculations in Summer & Winter
Coverage Area in Foggy Days
Coverage Area Calculation in Rainy Days
Coverage Area Calculations by Using Developed Okumura Model
Difference between Measured Field Data to Path Loss Model
Average Error between Currently Measured Data with Okumura
and Developed Okumura Model
Coverage Area Calculations Taking Different Parameters
154
155
160
172
173
175
175
179
181
182
184
188
188
189
xii
LIST OF ABBREVIATIONS
1G
First Generation wireless technology
2G
Second Generation wireless technology
3G
Third Generation wireless technology
1xDV
3G Extension of IS-95B: shared data and voice
1xDO
3G Extension of IS-95B: data only
1xEV
3G Extension of IS-95B: data with circuit-switched voice
1xRTT
3G Extension of IS-95B: one RF channel
ACELP
Adaptive Code Excited Linear Prediction
ADPCM
Adaptive Digital Pulse Code Modulation
AM Amplitude
Modulation
AMPS
Advanced Mobile Phone Service
BCCH
Broadcast Control Channel
BCH
Bose Chaudhuri Hocquenghem also Broadcast Channel
BoD
Bandwidth on Demand
BPSK
Binary Phase Shift Keying
BS Base
Station
BTS
Base Transreceiver Station
CC Convolution
Code
CB Citizens
Band
CDMA Code
Division
Multiple
Access
CEPT
Conference of European Postal and Telecommunications Administrations
COST
Cooperative for Scientific and Technical Research
CT2
Cordless Telephone 2
CTIA
Telephone Industry Association
COST WI
COST Walfisch Ikegami
DCS
Digital Cellular System
DECT
Digital European Cordless Telephone
DQPSK
Differential Quadrature Phase Shift Keying
DS-CDMA
Direct-Sequence Code Division Multiple Access
EDGE
Enhanced Data Rate For GSM Evolution
EIRP
Effective Isotropic Radiated Power
EFR Enhance
Full
Rate
ELF Extremely
Low
Frequency
ETACS
Extended Total Access Communication System
also European Total Access Cellular System
ETSI
European Telecommunications Standard Institute
EURO-COST
European Cooperative for Scientific and Technical Research
EHF
Extremely High Frequency
F/TDMA Hybrid
FDMA/TDMA
FDD
Frequency Division Duplex
FDMA
Frequency Division Multiple Accesses
FL Forward
Link
FM Frequency
Modulation
FR Full
Rate
xiii
GAN Global
Area
Network
GMSK
Gaussian Minimum Shift Keying
GFSK
Gaussian Frequency Shift Keying
GPRS
General Packet Radio Service
GPS
Global Positioning System
GSM
Global System for Mobile Communications
HF High
Frequency
HO Hand
Over
HR Half
Rate
HSCSD
High Speed Circuit Switched Data
HSPDA
High Speed Downlink Packet Access
HSPA
High Speed Packet Access
HSUPA
High Speed Uplink Packet Access
iDEN
Integrated Digital Enhanced Network
IMT
International Mobile Telecommunications
ITU
International Telecommunication Union
ITU-R
IS-54
ITU's Radio communications sector
EIA Interim Standard for U.S. Digital Cellular with Analog Control
Channel
IS-95
EIA Interim Standard for U.S. Code Division Multiple Access
IS-136
EIA Interim Standard136 USDC with Digital Control Channel
ISDN
Integrated Services Digital Network
JTACS
Japanese Total Access Communication System
LF Low
Frequency
LOS
Line of sight
LTE
Long Term Evolution
MSE
Mean square error
MF
Medium Frequency
MS Mobile
Station
NIDS
Network Intrusion Detection System
NMT
Nordic Mobile Telephone
NLOS Nonlineofsight
NTACS
Narrowband Integrated Services Digital Network
NTT
Nippon Telephone and Telegraph
OVSF
Orthogonal Variable Spreading Factor
PABX
Private Access Business Exchange
PDC
Personal Digital Cellular
PCNs
Personal Communication Networks
PN Pseudo
Noise
PCS
Personal Communication System
PSI-CELP
Pitch Synchronous Innovation CELP
QCELP
Quadrature Code Excited Linear Prediction
QoS
Quality of Service
QPSK
Quadrature Phase Shift Keying
RAN
Radio Access Network
RCELP
Residual Code Excited Linear Prediction
RL Reverse
Link
xiv
RPE-LTP
Regular Pulse Excited Long Term Prediction
SDCCH
Stand-alone Dedicated Control Channel
SQI Speech
Quality
Index
SHF Super
High
Frequency
TACS
Total Access Communication System
TCH
Traffic Control Channel
TDD
Time Division Duplex
TETRA
Terrestrial Trunked Radio
UHF
Ultra High Frequency
UMTS
Universal Mobile Telecommunication System
VHF Very
High
Frequency
VLF Very
Low
Frequency
VSELP
Vector Sum Excited Linear Prediction
WCDMA Wideband
CDMA
WiMAX
Worldwide Interoperability for Microwave Access
WARC
World Allocation Radio Conference
xv
CHAPTER 1
INTRODUCTION
In current era the wireless communication is spreading throughout the world
rapidly. The wireless technology has covered each and every area in day to day life.
This chapter discusses the historical overview and outline of the thesis along with
expected outcome of the research work carried presently.
1.1 HISTORICAL OVERVIEW
Wireless communication is one of the most dynamic and vibrant areas of
technology development in the communication field today. To give better
understanding, it may be revert from literature of old days that the first outcome of
communication started with origin of radio in the year 1680 by Newton's theory of
composition of light. According to Newton, light is a composition of various colours
and his theory brings the importance of light as a research area of study for many
scientists. Later on in 1873, James Clerks Maxwell gave many laws to explain electro
magnetism as a result of Poisson's equation using electrostatics, Gauss law equation
using magneto statics, Ampere's law equation using electrodynamics, and Faraday law
equation using magneto-dynamics. After his research, in the year 1888, Heinrich Rudolf
Hertz practically verified the electromagnetism phenomena which Maxwell obtained
mathematically [164].
Four years later, in the year 1892, A British scientist Sir William Crookes
published a paper on telegraphic communication over long distances using tuned
circuits. With the help of Crookes work, Gugliemo Marconi established a radio link
over a distance of a small number of miles in 1895. It is the first revolution to the
mobile radio industry. The communication with people on the move was made possible
by this radio link. Two way radio communication links at frequencies of 30 to 40 MHz
were designed from the middle of 1930s [174]. The radio communication gradually
increased to include the metric, decimetric and centimetric wavelengths from the year
1930 to 1960 [187]. From the year 1970 frequency modulation was introduced in
communication. The analog cellular systems were first developed by Bell Laboratories
[186]. In 1979, an effort was made to launch and install first cellular system, i.e.
Advanced Mobile Phone Service (AMPS) started at Chicago. Then in 1980 the High
Capacity Mobile Telephone System (HCMTS) launched at Tokyo and the Nordic
Mobile Telephone (NMT) launched in 1981 at Scandinavia. France's Radiocom 2000
1
was operational in 1985, similar to United Kingdom's Total Access Communication
System
(
TACS) and Germany's C 450 systems [209]. In the early days of 1990s, low
cost cordless system and it got remarkable growth rates. Among these systems cellular
played an important role in such growth process, especially after the invention of
international digital standards like Global System for Mobile Communications (GSM)
and Code division multiple access (CDMA) system (IS-95) [184].
In general, the cellular systems in operation are divided into two categories:
the first-generation analog systems and the second generation digital systems. At
present, one can observe the quick growth of different types of wireless communication
systems for example personal fixed & mobile, and land & satellite. These systems
utilize a frequency band from 500 MHz up to 3 to 10GHz. The IMT-2000 third
generation cellular mobile system was introduced in 2002.This system relies on cellular
techniques and reuses the basic concepts of architecture, functionality and services of
these systems [70], [187]. Generation wise the wireless communication is as shown in
figure 1.1. The first-generation (1G) mobile systems were analogue, and commissioned
in the 1980s. In the 1990s, second-generation (2G) digital mobile systems such as the
GSM came in existence. The GSM standard is tremendously triumphant, providing the
national as well as international coverage. So, GSM is nowadays the foremost mobile
communication system [163].
Figure 1.1 Year Wise Development of Wireless Communication
2
Wireless communication has gained incredible growth in the last few years. The
first mobile contributions took place in the early 1980s, and the industry was
blooming by 1987. However, the traditional phone technology was analogue. The
business take-off by GSM (digital) technology occurred in 1992. In early 1991
hardly one in every thousand people had a mobile phone. But till the end of 2001,
approximately 17% people got access of the mobile phone [106]. Within this
period the number of countries using a mobile network increased tremendously
from 3% to more than 90%. In 2002 the number of mobile subscribers leaves
behind the number of fixed-line subscribers. Mobile subscribers outnumbered by
7% fixed line subscribers: Mobile subscribers (million): 1,157 and Fixed lines
(million):1,083. Since 2002, the fixed line technology declined, getting closer to
the edge of obsolescence. The growth of mobile subscribers is depicted in figure
1.2. It is assumed that this growth will continue to rise, and by 2015 every person
will have mobile subscription [163].
Figure 1.2 Global Growths of Mobile Subscribers
Other than mobile phone communications, Wireless Local Area Networks
(WLANs), which came into existence in 1997 only, have also gained tremendous
growth. The quick propagation of WLAN hotspots in public places like airport terminal
has been amazing. In fact, WLANs have reached into homes, with the help of
Digital
subscriber line
(
DSL) and cable access modems resulting in the scenario where number
of wireless Internet subscribers will go beyond the number of wired internet users in
near future and shown in figure 1.2. The growth of wireless data systems is also seen in
many new standards which have recently been developed or are currently under
development [163]. Both 1G and 2G systems were intended mainly to offer voice
3
applications, and to support circuit-switched services [167]. However, GSM provides
data communication services to users, but the data rates are restricted to only a few tens
of kbps. In contrast, WLANs which were designed to offer fixed data network extension
in the beginning provide Mbps data transmission rates. The WLAN standard IEEE
802.11, known as Wi-Fi, was commissioned first time in 1997 and it offered 2 Mbps.
Since then the standard has grown numerous times and keeps on increasing as per user
requirement for higher bit-rates as shown in Table 1.1. Now days, WLANs can offer
up-to 54 Mbps for the IEEE 802.11a/g, and Hiper LAN2 standards operating in the 2.4
GHz and 5 GHz license-free ISM bands. Though, WLANs are not able to provide the
kind of mobility, which mobile systems can do [5], [163].
Table 1.1 Evolutions of the WLAN Standards
Year of
Establishment
Standard of WLAN
Standard
Frequency
Modulation
Bit rate
1997
IEEE 802.11
2.4 GHz
Frequency Hopping
and direct spread
spectrum
2 Mbps
1998
ETSI Home RF
2.4 GHz
Wideband Frequency
Hopping
1.6 Mbps
1999
IEEE 802.11b
2.4 GHz
Direct Sequence
Spread spectrum
11 Mbps
1999
IEEE 802.11a
5 GHz
OFDM
54 Mbps
2000
ETSI Hiper LAN2
5 GHz
OFDM Connection
oriented
54 Mbps
2003
IEEE 802.11g
2.4 GHz
OFDM Compatible
with 802.11a
54 Mbps
Wireless communication should be designed to attain high capacity with
limited radio spectrum and it is possible by the Cellular radio concept, which is
discussed in the following section.
1.2 CELLULAR RADIO CONCEPT
The concept of cells was introduced in early 1947 by Bell Laboratories in
the US; they also gave a detailed proposal for a "High-Capacity Mobile Telephone
System" integrating the cellular concept submitted by Bell Laboratories to the FCC in
1971. Still the first AMPS system was set up in Chicago in 1983 [56]. The old system
was able to attain a large coverage by means of a simple, high power transmitter in a
cell. Base station (BS) was put on the top of mountains or tall towers, so that it could
cover a large area. The next Base station BS was put so far away that interference was
not a concern. Wireless radio services just in terms of spectrum use alone pretence a
much more difficult problem [33]. Severely, it bounds the number of users that could
communicate at a time. These were noise-limited systems as numbers of users were
limited. The Bell mobile system in New York City in the 1970s was able to
communicate a maximum of twelve calls at a time over an area of thousand square
4
miles [125], [186]. The number of calls a mobile wireless system can handle at the same
time is essentially determined by the total spectral allocation for that system and the
bandwidth needed for transmitting signals used in managing a call. Cellular systems can
handle a large number of users over a large geographic area within a limited frequency
spectrum. High capacity is attained by using the concept of cell which is a small
geographic area and for each cell a single base station is used. Using this concept the
same radio channels can be reused by another base station situated some distance away.
The entire coverage area can be partitioned into several cells [14]. A cell corresponds to
the covering area of single BS transmitter or a small collection of many transmitters.
The size of a cell is determined by the transmitter's power.
In this way a single, high power transmitter (large cell) is replaced by many
low power transmitters (small cells) which cover only one cell area (a small portion of
the service area) as shown in figure 1.3. For mobility a sophisticated switching
technique called handoff is used which helps in establishing a call un-interrupted when
the user shift, from one cell to another.
Figure 1.3 Illustrations Showing the Importance of Accurate Coverage Estimation in
Cellular Networks as Compared to Early Land to Mobile System
Basic cellular system consists of mobile stations, base stations, and a mobile
switching centre (MSC). Mobile switching centre (MSC) is also referred as mobile
telephone switching office (MTSO) which manages the activities of the base stations
and also connects the entire cellular system to the public switched telephone network
(PSTN) [230]. It handles all billing and system maintenance functions. Each
communication takes place via radio waves with one of the base stations and for the
complete duration of call the mobile station may be handed-off to any number of base
stations [231]. Mobile station consists of three units, first one is transceiver, second is
an antenna, and third is control circuitry. Among all mobile users in the cell Base
stations work as a bridge and helps in connecting the concurrent mobile calls via
telephone lines or microwave links to the MSC. It contains a number of transmitters and
receivers which concurrently manage full duplex communications. In general it has
towers to support numerous transmitting and receiving antennas [232]. Cellular concept
also depends on an intelligent allocation and reusability of channels all over a coverage
5
region. These systems are sometimes referred as narrow band systems as these use the
concept of frequency reusability. The frequency reuse concept is given in the following
section.
Figure1.4. First Generation Cellular Phone of 1924
1.2.1
Frequency Reuse
Cellular notion depends on an intelligent allocation and reusability of
channels all over a coverage region. These systems are sometimes referred as narrow
band systems as these use the concept of frequency reusability. A group of radio
channels are assigned to each cellular base station (BTS) to be utilized within a cell.
The design process contains selecting and assigning channel groups to all cellular BTS
within a system [80]. Consider a cellular system has a total of S duplex channels
available for use. If each cell is allocated a group of k channels, where k< S, and if the S
channels are divided among N cells into unique and disjoint channel groups which each
have the same number of channels, the total number of available radio channels can be
expressed as
S= k N (1)
6
Figure 1.5 Concept of Frequency Reuse
The N cells which collectively use the complete set of available frequencies
is called a cluster [90]. If a cluster replicated M times within the system, the total
number of duplex channels C can be used as a measure of capacity and is given by
C=M k N= M S (2)
The factor N is called cluster size (typically equal to 4, 7, or 12) and it is a
function of how much interference a mobile or base station can tolerate while
maintaining a sufficient quality of communications [208]. The frequency reuse factor is
given by N, since each cell within a cluster is only assigned of the total available
channels in the systems. The number of cell, N can be related to the geometry of the
hexagons as given below:
N = i
2
+ ij + j
2
(3)
It means that one has to move i cells along any chain of hexagons and the
turn 60°counter-clockwise and move to j cells.
B
C
G
A
B
A
D
F
E
G
E
F
B
E
C
A
C
G
C
D
G
F
E
D
Cluster
7
Figure 1.6 Illustration of Frequency Reuse Concept
1.3 CONCEPT OF HANDOFF
When a mobile unit is moving from one cell area to another cell area while a
call is in progress, the mobile switching centre (MSC) automatically transfers the call to
a new cell area belonging to the new base station [177]. It is defined as the process of
changing the current radio channel to a new radio channel [221], [222]. It is a perfect
service to all mobile phone consumers while data transfer is in progress. Handoff is an
expensive process to execute, so unnecessary handoff should be avoided while ensuring
that essential handoffs are finished before a call is terminated due to poor signal level.
Handoff includes two major steps; first handoff initiation; In this initiation phase,
decision to start the handoff procedure is taken. Second is handoff execution; in this
execution phase, a new channel assignment is carried out or if there is no channel
available the call is dropped [47], [41]. Basics of handoff are given in the figure 1.6.
Figure 1.7 Basic of Handoff
8
1.4 CONCEPT OF TRUNKING
Cellular systems depend on trunking to hold a large number of cellular
subscribers in minimum number of channels. The concept of trunking allows a large
number of subscribers to share a relatively minimum number of channels by providing
access to each user from available channels. In a trunked system, subscriber is assigned
a channel on a per call basis, and upon termination of the call, the previously occupied
channel is immediately returned to the available channels. The grade of service (GOS)
is a measure of the ability of a user to access a trunked system during the busiest hour of
call traffic. It is clear that there is a trade-off between the number of available channels
and the possibility of a particular user finding that no channels are available during the
peak calling time. The number of channels required is determined based the number of
subscribers, desired GOS, average call holding time and traffic distribution with time.
Detailed description and problem statement of this thesis is presented in the
next section.
1.5 STATEMENT OF PROBLEM
It is investigated and found that severe climatic conditions disturb
propagation of electromagnetic signals at higher frequencies [greater than 30MHz] [52].
The disturbance is mainly due to molecular absorption by oxygen for frequencies
ranging between 60 and 118 GHz and due to water vapour in 22, 183 and 325-GHz
bands [209]. Rain and fog has the most significant impact since the size of the rain
drops is of the order of the wavelength of the transmitted signal. It results in energy
absorption by the rain drops themselves, and as a secondary effect energy is scattered by
the drops. The frequency selective absorption characteristics of the atmosphere can be
approximated by a transfer function [95], [131]. In most of practical channels when the
signal propagates through the atmosphere affect of many factors on the signal has to be
considered along with the free space propagation channel assumption.
Due to those practical channels the incoming radio signal enters the receiver
circuitry varies in magnitude. These variations lead to changes in propagation
conditions. In acute cases it can even lead to complete cancellation of a signal at the
receiving point. These signal variations can occur fast or slow and the speed at which
they take place is known as "rate of fading" the reception of microwaves depends on
their propagation between a transmitter and a receiver [162]. In the Narnaul city of
Haryana (India), the atmosphere is seasonally affected by summer, winter, rain and fog.
These different climatic conditions affect the radio wave propagation. The rain and fog,
out of these four different climatic conditions plays an important role.
The main objective of this thesis is, therefore, to find out whether, and how,
the different climatic conditions are influencing radio wave propagation in GSM band
9
in general and Narnaul, Haryana (India) in particular. To carry out this investigation, the
records of radio wave propagation along with path loss during different climatic
conditions will be analysed. On the strength of these analyses, a propagation path loss
model has been developed by proposing suitable correction factors due different
climatic conditions. The validation of this developed path loss model has been verified
by taking reference models and by applying practically in different urban area. The
effect of these climatic conditions on link budget has been analysed [52].
1.6 THESIS MOTIVATION
The wireless Communication systems: such as radar system, radio
navigation, mobile communication, remote sensing, control and recording use radio
frequencies as a fundamental transmission medium for their operations.
The need for assessing different climatic conditions such as summer, winter,
rain and fog on radio wave propagation is for monitoring of the radio signals and
measurements of their field strength and fading characteristics will be analysed. This
investigation can also lead the radio planner to a thorough understanding of radio wave
propagation in the Narnaul, Haryana (India) for designing mobile communication
system. Now, literature review of the thesis will be presented.
1.7 LITERATURE REVIEW
Propagation models are nothing but the combination of analytical and
empirical methods. These models are used to calculate electromagnetic field strength
for the purpose of wireless network planning during initial establishment. The Harald.T.
Friis free space model is mainly used to predict the signal power at the receiver end
when transmitter and receiver have line-of-sight condition [186].
The real work on propagation model was initiated in the year 1968 by
Okumura. Okumura gave a model to calculate signal strength by collecting
measurements surroundings of Tokyo city at frequencies up to 2GHz [168]. The basic
source of his model was taken from the free space path loss model. Okumura added the
median attenuation (A
mu
) to the free space path loss in an urban area with a base station
height (h
te
) of 0.2km and a mobile antenna height (h
re
) of 0.003km. The median
attenuation is expressed as a function of frequency (0.1 3 GHz) and the distance from
the base station (1 -100 km) to the receiver. He also observed and gave modification
factors for base station (transmission) antenna G(h
te
) and mobile antenna (reception)
G(h
re
). He has obtained some other modification factors in graphical form in suburban
and rural area along with urban area [98], [157]. Addition or subtraction of these factors
depends on the surroundings and various situations. The entirely empirical nature of the
Okumura model shows that the parameters such as frequency, antenna height, range,
type of environment, size of the city and the street orientation are restricted to exact
ranges determined by the calculated data on which the model is based. This proves that
10
prediction can lead to impractical results if the one or more parameters are used outside
the range. Some other constraints also exist with the terrain related parameter.
In the same vein, Hata made some efforts to make the Okumura model easy
to apply and establish an experimental mathematical relationship [89] which describes
the graphical information given by Okumura. All graphical relations are replaced with
mathematical relationships. The problem in Hata's model is its mathematical
formulation i.e. it is limited to certain ranges of input parameters. The difference
between prediction given by Hata's equation and Okumura curve reveals slight
differences that infrequently exceed 1 dB [89]. It was concluded that Hata's method is
fairly superior in the urban and suburban areas, but not superior in the rural areas.
William C. Y. Lee gave a model (Lee model) [230] to acquire UHF band
propagation characteristics over irregular terrain by use of two approaches: first an area
to area algorithm. This approach is based on the equation of straight line presentation
of path loss by use of the following parameters: average transmission loss at the
range of 1 km, slope of the path loss curve according to plane earth model and an
adjustment factor. The standard deviation in predicting the average path loss of this
algorithm is 8 dB. Second a point to point algorithm, this approach takes terrain profile
into account and it better predicts the variations of the terrain surface. The standard
deviation of this algorithm is less than 3 dB.
In the year 1994 European Cooperative for Scientific and Technical
research (EURO-COST) has projected COST 231 model to overcome the restrictions in
Hata model like frequency range (restricted from 150 MHz to 1500 MHz). In order to
accomplish this goal, under COST 231 a huge amount of propagation measurements
were performed in the 900 MHz band and 1800MHz band in a large variety of different
environments ranging from Pico cells to micro cells and from micro cell to macro cells.
Different measurement techniques which are described by J. B. Andersen [25] were
used. Much attention was paid to urban micro cellular investigations and to indoor
investigations because these cell types will be particularly important for future
(Universal Mobile Telecommunications System
)
UMTS systems.
According to the different objectives explained above, diverse approaches
for the classification of measurements were used. Measurements were considered as
function of the cell size (e.g. Pico, micro, macro cells), as function of the base station
visibility (LOS, NLOS), as function of terrain folds (flat, hilly, etc.), as function of
building (large, small) or vegetation density, as function of the mobile speed (from
stationary to high speed trains), etc.. Based on this wide-ranging measurement
campaigns in European cities, COST 231 has investigated different existing models and
has created two new propagation models i.e. COST 231 Hata and COST231 Walfish
Ikegami model. These models are suitable for flat terrain and based on the approaches
of Walfisch-Bertoni [228], Ikegami and Hata model. The COST 231 Hata model is the
extension of Hata model by analyzing Okumura´s propagation curves in the upper
11
frequency band and it is limited to macro cell where the base station antenna is on top of
the rooftop level of adjacent building.
The COST 231 observed that the evaluation of path loss agrees to a certain
extent well with the measurements for base station antenna heights above rooftop level.
The mean error is in the range of +3 dB and the standard deviation 4-8 dB [134]. Multi
path propagation does not consider in COST-WI .it was observed that the consistency of
path loss evaluation decreases if terrain is not flat [135].
A. Ghosh, J. G. et al [76] suggested a way in Broadband wireless access
with WiMax/802.16: current performance benchmarks, and future potential. C. F. Ball,
et al [27] explained the basic IEEE802.16 (WiMax) 256 sub-carrier OFDM
performance for a 3.5 MHz channel in the 3.5 GHz band by link and system level
simulations in both interference and coverage limited cellular mobile environment.
Gilhousen K.S, et. Al carried out his research in power-controlled multiple-
cell CDMA to increase the cellular capacity [76]. Greg Durgin., and Theodore S.
Rappaport., has concluded their results of path loss and building penetration loss
measurements in residential areas. Their work determined the effects of shadowing,
house construction, and floor plan on the penetration of radio waves into homes [82].
Constantino Perez-Vegay., et al [46] worked on power law path loss model
for indoor communications at 1.8 GHZ. In his research he mentioned that the exponent
of the distance is treated as a random variable and its behaviour is studied through
experiments conducted under a variety of propagation conditions in various buildings.
K. Smitha et al [204], presented a modification in ceiling bounce method to find the
propagation properties of the channel. Her results clearly show that path loss is a
function of separation between the transmitter and receiver.
Aliye ¨Ozge Kaya et al, also Worked on indoor propagation models and
gave a New Path Loss Modeling Approach for wireless Networks inside buildings. The
proposed model describes log of diatance will lead to nonlinear-curve-fitting for
pathloss [12].
Nagendra Sah. et al [156], surveyed on basic solving techniques behind
constraint programming. In particular they concentrated on constraint satisfaction
algorithm that are use to solve the constraint satisfaction problem. In his work he
focused on various wireless empirical propagation models and solved to find the
propagation loss using the constraint satisfaction algorithm.
Comparison of path loss propagation models at 3.5 GHz has been carried out
by many researchers in many aspects. V.S. Abhayawardhana et al, worked on this area
12
in Cambridge, UK from September to December 2003 [2]. Josip Milanovic, Rimac-
Drlje S, Bejuk K, investigated some empirical propagation models in different terrains
as function of antenna height parameters [118]. Basharat worked on CDMA versus
IDMA subscriber cell density and explained that beyond third generation (B3G) and
fourth generation (4G) communication systems require bandwidth efficiency and low
complexity receivers to accommodate high data rate and large number of users per cell.
He provided the recommendations for why interleave division multiple access (IDMA)
stands out among all the present day multiple access systems [28].
Ubom, E.A., Idigo, V. E., Azubogu, A.C.O., Ohaneme, C.O., and Alumona,
published a paper in which he has suggested statistical path loss models derived from
experimental data collected in Port Harcourt in South-South region of Nigeria from 10
existing microcells operating at 876 MHz. The results of the measurements were used to
develop path loss models for the urban (Category A) and the suburban (Category B)
areas of Port Harcourt [220]. M. A. Alim, M. M. Rahman, M. M. Hossain, A. Al-Nahid
explained in their paper that Channel properties influence the development of wireless
communication systems. They also explained that in mobile radio systems, path loss
models are necessary for proper planning, interference estimations, frequency
assignments and cell parameters in a wireless system [11]. Purno Mohon Ghosh, Md.
Anwar Hossain, A.F.M. Zainul Abadin, Kallol Krishna Karmakar Many Path loss
models for macro cells like Hata Okumura, Walfisch-Ikegami and Lee. The received
signal strength was calculated with respect to distance and model that can be adopted to
minimize the number of Handoffs [75].
Mohammed Alshami, et al, analyzed and compared the path loss values and
determined the link budget, power outage probability and WiMAX cell coverage area.
His research work discusses and implements WZ Okumura, Hata, Cost- 231, Ericsson,
Erceg, Walfish, Ecc-33, Lee and the simplified free space path loss models. All the
models applied in his paper are used to predict the propagation loss at WiMAX cell-
edge [153]. According to ECC Report 33, [52] the analysis of the existence of FWA
cells is in 3.43.8 GHz frequency band. The level of this Report give procedure for
efficient, technology independent operation of 3.5 GHz (or 3.7 GHz) Point-to-
Multipoint (PMP) Fixed Wireless Systems (FWS) [62].
Julio C. Costa, prepared his thesis to share some insight on the propagation
characteristic of the radio path in the Tampa Bay area. modified models in the Tampa
Bay area were presented, including a specific modified model to support bridges in the
Tampa Bay area. These models will help more accurately predict coverage and
interference within the area [120]. Pu wang., et al [180] explained about sand storm
effects on signal strength of wireless communication signals in their signals. Many ideas
from "S. Dey and J. Evans, about Optimal power control over multiple time-scale
fading channels with service outage constraints is given for parallel fading channels
with fast Rayleigh fading, as a function of the slow fading gains [54].
13
Tapan K Sarkar., et al [211], made a survey on various propagation models
that can provide good estimates for fading channels. Adegoke et al. (2008) did an
evaluation of the performance of GSM operators using Nigeria as a case study and
examined the problems in front of the industry. Their main focus devoted on improving
the performance of the network elements [4]. Adebayo T.L. et al. investigated the
propagation path loss characteristics of GSM signals in Benin city, Nigeria using 15
different environments. Later the data was analyzed to calculate path exponent [3].
Gorazd kandus et al. presented a paper on pathloss analyses in tunnels and under ground
corridors and it is very useful in pathloss analysis [81]. Andreas F. Molisch gave
definition of pathloss as the average attenuation (reduction in power) of a radio signal as
it propagates and includes the propagation losses caused by free space and effects due to
absorption, diffraction, and others [18].
E.Reusens., et al [189], developed Path loss models for the on body
channels. J. De Bruyne, et al [57] investigated the actual measured performance of an
802.16-based system. A measurement methodology for evaluating the performance is
proposed, which is then used for studying and comparing the results of different
scenarios. More exclusively, the influences of varying the modem height from 2.5 m to
6 m and base station height from 15 m to 45 m are analysed and discussed in this paper,
and it will be shown that only the latter one has a significant effect on the coverage and
the performance. Finally, as the system supports link adaptive modulation and coding,
the results of its effectiveness are discussed.
Liao D. and K. Sarabandi., [130] calculated the far-field radiation from an
infinitesimal electric dipole embedded inside a truncated vegetation layer above a
dielectric ground plane. Lorne C. Liechty [133] carried out an experiment at campus of
the Georgia Institute of Technology. Using the measurements in that area, they
observed that a simplistic direct-ray, single path loss exponent, adaptation of the Seidel
-Rappaport model can yield satisfactory results in terms of accuracy of model for
outdoor microcell environments. Armoogum V., et al [19], made a qualified Study of
Path Loss using active Models for Digital Television Broadcasting for Summer Season
in the North of Mauritius and the Results showed that the path loss is not constant at
various locations for a constant distance around the base station.
Hazer Inaltekin., et al [91] , explained the effects of the singularity in
unbounded path-loss models on network performance. Meng, Y. S. et al [147], [148],
[149], worked in forest environment and explained radio wave attenuation in those
environments. His report gives information about the physical processes when the radio
signals propagating through a deep forest. Tan I., et al. presented a GPS-enabled
channel sounding platform for measuring vehicle-to-roadside wireless channels. This
platform was used to conduct an extensive field measurement campaign involving
vehicular wireless channels across a wide variety of speeds and line-of-sight conditions
[210].
14
Turkan ERBAY DALKILIC.¸ et al [218] completely worked on Fuzzy
adaptive neural network approach to path loss prediction in urban areas at GSM
frequency (900 MHz) band. Lkhagvatseren. T., and Hruska. F., [132], explained
propagation of RF signal from frequency 1 to 8 GHz range. Noman Shabbir., et al ,
published about the radio propagation models used for the upcoming 4th Generation
(4G) of cellular networks. The radio wave propagation model or path loss model plays a
very vital role in planning of any wireless communication system. In his paper, a
comparison is made between different proposed radio propagation models [166]. Zhi
ren., et al, studied the effects of Rayleigh fading, path loss, and shadowing fading on
wireless mobile networks and implement ed modelling and simulation of Rayleigh
fading and shadowing fading with OPENT in their paper [238]. The literature survey on
related work of this thesis is presented in the following section.
1.7.1 Related Work
1.7.1.1 Field Propagation Path Loss Models
In 2008 Faihan D. Alotaibi and Adel A. Ali [15], [16], [17] has introduced
modification of the Lee path loss empirical model using an automatic LS algorithm.
Their calibration is based on conventional signal measurements taken in Riyadh, Saudi
Arabia on TETRA network. To verify the LS algorithm method and for reasonable
performance estimation they have compared the measured signals against predicted
ones using the tuned model in addition to the three most widely used empirical path loss
models these are Hata, ITU-R and COST-WI NLOS. They found that performance of
the tuned Lee model is the best, as root mean square error is the lowest compared to the
other mentioned models. Also they have found that Hata, ITU-R and COST-WI NLOS
empirical models expect too much of the path loss for both urban and suburban
environments. It is obviously clear from their studies that the tuned Lee model shows
the closest agreement with the measured results, while ITU-R and COST-WI NLOS
models perform better than the Hata empirical model.
A semi empirical propagation model for the frequency band from 850 MHz
to 900 MHz has been proposed by Juan M. Casaravilla and Gabriel A. Dutra in 2009.
To evaluate the performance of the MOPEM model against the COST-WI model, Juan
has made a close comparison between measurements of MOPEM model and COST-WI
model. The outcome reveals that proposed model i.e. MOPEM is more precise than
COST-WI model for the region. The author also recommended significant
modifications for COST-WI model with regard to the reference model i.e. MOPEM
model [38]. Vinko Erceg., et a has presented a statistical path loss model for 1.9-GHz
wireless systems in suburban environments. The path loss it predicts can be either the
local mean (time-averaged) value for a mobile system or the broadband value for a
fixed system. The model makes distinctions among different terrain categories. The
result is a general statistical framework for describing path loss that can be upgraded
with further measurements [224], [225].
15
Zia Nadir [157], [158], [159] has investigated Hata model for GSM band in
Oman. For survey he conducted the measurement in Salalah, Oman with the help of
TEMS tool. After determining the path loss of the practical measurements for each
distance, the study was carried by him, in order to make a comparison between the
measurement and Hata model. The comparison results clearly explain that the measured
path loss is less than the predicted path loss by a difference varying from 4 to 20 dB.
Then, mean square error (MSE) was calculated between measured path loss value and
those predicted by Hata model. The mean square error (MSE) was found 112.459dB but
the acceptable range is up to 6 dB. Zia Nadir investigation shows that Hata propagation
model may not be completely adapted in Oman so improvement of Hata model in the
open area has been recommended.
In 2010, R. Mardeni and K. F. Kwan [142], [143], [144] analysed the Hata
model, Egli model and COST-WI model. They have taken the outdoor measurements in
Cyberjaya, Malaysia in order to make a path loss comparison with these existing
models. The frequency ranges used for measurement are from 400MHz to 1800 MHz,
covering CDMA [121], GSM900 and GSM1800 technologies. From the comparison
they found that the performance of the Hata model is the superior as compared to other
mentioned models. Generally, COST-WI and Egli model present better than Hata for
suburban area in Malaysia. Roelens , in 2005 worked on pathloss models for wireless
narrow band communication near biological tissue [191].
Shoewu, O and Adedipe, A. [200], [201] shows that the Hata model for
radio wave propagation is very efficient for radio wave propagation path loss prediction
in suburban areas in Northern part of Nigeria. They have used a GSM base station
operating at 900MHz band for the experiment in a typical suburban area within the
Northern part of Nigeria. The field measurement results were compared with Hata
model for rural and suburban area. The outcome obtained by them point out the slightest
variation with Hata model for suburban areas.
In 2010, Mardeni, R and Lee Yih [141] investigated the Free space model,
Okumura model, Egli model and Hata model for urban outdoor coverage in Kuala
Lumpur, Malaysia for Code Division Multiple Access (CDMA) system. Mardeni, R and
Lee Yih conducted a measurement test in Kuala Lumpur for CDMA system.
Subsequent to the contrast between above model and test result, they have found that
Okumura model is the preeminent for CDMA in the region. In [239], Zhu and McNair
presented cost functions that account for the dynamic values that are inherent to vertical
handoff and incorporate a network elimination factor to potentially reduce delay and
processing power in the handoff calculation.
16
1.7.1.2 Effect of Climatic Conditions on Radio Communication
Abdullahi, mainly concentrated on the wireless network features and their
effects on radio propagation quality [1]. Olagoke worked on traffic of NITEL GSM
network. Main features of his work are network Optimization, network monitoring and
network handling by continuous measuring, and analysing the traffic data [169]. In the
year 2001, Isaac I. Kim, Bruce McArthur, and Eric Korevaar, worked on laser beam
propagation at 785nm and 1550nm in fog and haze for optical wireless communication
and found 785nm, 850nm and 1550 light suffer from atmospheric attenuation. Their
observation of wavelength, attenuation in fog is important, because fog, heavy snow,
and extreme rain are the only types of climates that can disturb communication links
[101].
J. A. Weinman, R. Davies and R. Wu, concluded that
Water or ice particles
blown from the ground into the atmosphere
take the form of liquid water as in rain, and
fog as in clouds. electromagnetic waves travelling through air containing precipitation
are scattered and absorbed by the particles of ice, snow or water. Water has larger
dielectric constant and it scatters electromagnetic wave more strongly than ice
[229,234]. In addition to above conclusion Akira ishimaru in 1978, gave a conclusion
that dielectric loss and the attenuation due to thermal dissipation is greater for water
particles than for ice particles. The conclusion given by Akira ishimaru is again
discussed in 2004 by Jonathan H. Jiang and Dong L. Wu [103], [113].
David M. Pozar , in his book mentioned that, attenuation is caused by the
absorption of microwave energy by tropospheric gases when the frequency coincides
with one of the molecular resonances of water or oxygen in the atmosphere [179].
Transmission of microwave signals above l0 GHz is vulnerable to precipitation, as has
been shown by many researchers over several decades [43], [65]. In his work he
considered radio path where the Fresnel zone is partially filled with rain droplets. Each
particular raindrop will contribute to the attenuation of the required signal. The actual
amount of attenuation is dependent on the frequency of the signal and the size of the
raindrop. The two main causes of attenuation are scattering and absorption. When the
wavelength is fairy large relative to the size of raindrop, scattering is predominant.
Conversely, when the wavelength is small compared to the raindrop size, attenuation
due to absorption is dominant [110].
Frey in his paper explained that Propagation of radio waves above 10 GHz
through the atmosphere is greatly influenced by effects of molecular resonance and
precipitation [212]. Lakshmi Sutha Kumar, Yee Hui Lee, and Jin Teong Ong concluded
that lower rain rates, does not affect the communication links [129]. Due to the applied
high carrier frequency (above 20 GHz) besides the existing interference and noise the
main degrading factor in these systems is attenuation caused by precipitation, especially
rain attenuation [104]. Dougherty H.T. et al, includes attenuation due to both rain and
gases. Dutton has developed an updated computer program to predict the rain
attenuation, cloud attenuation and attenuation due to atmospheric gases [58]. The rice
17
and Holmberg gave model which is based upon two rainfall types: convective ("Mode
l", thunderstorm) rains and stratiform ("Mode 2", uniform) rains [233].
Crane and Blood gave a Prediction Model which includes path averaging
implicitly, and adjusts the isotherm heights for various percentages of time to account
for the types of rain structures which dominate the cumulative statistics for the
respective percentages of time. Both forms will be described here because the latter is
the recommended form for use by system designers, but the earlier form is
computationally easy to implement and allows rapid computation with a hand-held
calculator [50].
In 2009 Shkelzen Cakaj analyzed the rain attenuation impact on the
performance of the respective ground station [36]. G. H. Bryant, I. Adimula, C. Riva and G.
Brussaard described a new model for determining rain attenuation on satellite links and
explained Rain attenuation statistics from rain cell diameters and heights [35].
Asoka Dissanayake et al, results indicate that the rain attenuation element of
their model provides the best average accuracy internationally between frequencies
10GHz and 30 GHz [22]. Oyesola Olayinka Olusola in his thesis gave a case study on
mobile radio wave propagation and Shittu (2006) carried his research to cellular mobile
radio propagation characteristics within urban and rural environments. He explained the
effect of various propagation losses on GSM signals [170], [198]. Bruce (2006) did
research on the prediction of seasonal effects on cellular systems in the United States.
His work mainly focussed on the effect of foliage in wet seasons on radio wave
propagation [34]. Helhel et al. considered the effect of dry and wet seasons on GSM
signals. The research conducted in Turkey inside a forested area. The losses observed in
his experiment were foliage losses. These losses (Dry and Wet seasons) were compared
and gave a conclusion that losses are greater during the wet season [92].
Fraizer explained the effects of foliage on the propagating signal in his
research [68]. Mohammed et al. (2006) took many measurements for signal attenuation
through Date Palm trees in North Abu Dhabi, United Arab Emirates. His measurements
in this region showed significant additional losses of up to 20 dB due to foliage [152].
Douglas (1973) did his research during two different seasons in a suburban area of the
United States of America found that the presence of increased foliage reduced the
received signal strength to typical values of about 10 dB and he stated that foliage is a
significant feature which affects propagation in suburban and rural areas but can be
neglected in most urban areas [59].
Batariere M.D. et al. continued the similar work and carried out
measurements of path loss due to tree foliage for a carrier frequency of 3.676 GHz in
outside Chicago, Illinois. He found lots of difference in path loss calculations in
between Urban and rural areas, Because rural area consists more foliage than urban
areas and in the other case Urban areas have more high-rise buildings. These differences
cause losses in radio propagation characteristics between urban and rural areas [29].
Bruce (2006) stated that the effect of foliage losses would be higher in rural areas
comparison with urban areas. Bruce's studies however did not focus on this difference
18
[34]. Vaclav Kvicera and Martin Grabner explained that the influence of climatic
conditions effect the electromagnetic signals all the way through the medium of the
lower troposphere. He gave many suggestions for efficient planning and utilization
[127]. The aim of Shkelzen Cakaj is to analyse the rain attenuation impact on the
performance of the respective earth station. Rain attenuation depends on geological
location where the satellite ground station is implemented [36].
M. Sridhar et al mentioned various impairments which causes the signal fade
in his technical paper and also explained that rain attenuation is dominant in those
impairments. He used ITU-R model to predict the rainfall rate and attenuation due to
rain [206]. Dong you choi presented the results of measurements of rain-induced
attenuation in vertically polarized signals propagating at 12.25 GHz for the duration of
definite rain events [wet season of 2001 and 2007 at Yong-in, Korea]. The rain
attenuation over the link measured practically and compared with loss/attenuation got
by the ITU-R model [101]. Kiran Ahuja and Manoj Kumar explained the importance of
empirical & physical propagation models to calculate the path loss by predicting the
received signal strength at a specific point in space by considering the particular
propagation surroundings. They considered Free Space, FCC, ITUR370, Okumura
(HATA) and physical models viz. TIREM, Anderson 2D v1.00 in their experiment [8].
1.7.1.3 Effect of Climatic Conditions on Link Budget
T.-S. Chu, and Larry J. Greenstein, carried out his research in link budget
analysis and explained that the median propagation loss in the personal communication
services (PCS's) band is greater than in the cellular band. They carefully examined all
factors involved to quantify the link budget differences between two bands in three
different terrains (urban, suburban, and rural.) and found that link budget parity can be
accomplished in all three environments with quite reserved remedies. This remedy
includes the use of tower-top electronics and minor increases in downlink power [45].
Peter J. Black and Qiang presented his paper on link budget of cdma 2000 1xev-do
wireless internet access system. It contains the analysis and simulation results for a
1xEV-DO link budget and also contains the traditional fixed rate CDMA link budget
calculation including link adaptation and multi-user diversity gains. The main
conclusion of his paper is that 1xEV-DO provides a link budget advantage over IS-95-A
link [32].
K.Ayyappan, P. Dananjayan prepared his paper to share some insight on the
propagation characteristic of the radio path in Pondicherry, INDIA and gave a
conclusion that Radio propagation is necessary for up- coming technologies with
proper design, operation and management strategies for several wireless networks.
Exact description of radio channel through key parameters and a mathematical model is
significant for predicting signal coverage. Path loss models such as Hata Okumura,
COST 231 and ECC 33 models are analyzed and compared their parameters. This paper
proposed a path loss model for a highway between Pondicherry Villupuram. Analysis
also included the link budget calculation [24]. In the same vein
P.Saveeda,E.Vinothini,Vardhi Swathi and K.Ayyappan continued this research and
concluded that Radio propagation profoundly site specific and varies considerably
19
based on many parameters. Some other constraints also exist with the terrain related
parameter. They analyzed and compared the path loss values and determined the link
budget [194].
Dr. S. A. Mawjoud studied about the parameters which affects the
communication performance of a wireless channel. He took a basic cell, by estimating
the affecting parameters on the signal power level in the uplink and downlink at all
practical circumstances considering the factors causing fading and other losses in the
signal power [145]. Dr. Joe Montana, Dr. James W. LaPean and Dr. Jeremy Allnutt
explained link budget in their way along with the effects of atmosphere on link budeget
[114].
Joshua D. Griffin and Gregory D. Durgin worked on Complete Link Budgets
for Backscatter-Radio and RFID Systems. They explained that communication by
modulating signals scattered from a transponder (RF tag) - is basically different from
conventional radio. Conventional radio involves two distinct links: the power-up link
for powering passive RF tags, and the backscatter link for describing backscatter
communication. Their article presented four link budgets which can give explanation
for the major propagation mechanisms of the backscatter channel. they demonstrated
the Use of the link budgets by a practical UHF RFID. The benefits of future 5.8 GHz
multi-antenna backscatter-radio systems are shown [83].
LUIGI MORENO explained fundamental parameters useful to describe
radio site installations in his book "POINT-TO-POINT RADIO LINK
ENGINEERING". The whole book covers the main topics in Radio Propagation and
Point-to-Point radio link engineering [136].
Sebastian Büttrich, concluded in his work that A good link budget is the
basic requirement of a well functioning of a radio propagation link. He mentioned many
Losses takes place in every element along the radio wave signal transmission path
[195]. Tranzeo wireless technologies provide the reader with an overview on how a link
budget is calculated. They also explained that Available and permitted output power,
available bandwidth, receiver sensitivity, antenna gains, radio technology, and
environmental conditions are some of the major factors that may impact system
performance. For large scale network operations, detailed site survey and network
designs are highly recommended like link budget analysis [216].
The coverage area, link budget and its calculations are discussed in the
following section.
20
1.8 CONTRIBUTION OF THESIS
By combining analytical and empirical methods the field propagation models
is derived. The field Propagation models are used for calculation of electromagnetic
field strength for the purpose of wireless network planning during preliminary
deployment. It describes the signal attenuation from transmitter to receiver antenna as a
function of distance, carrier frequency, antenna heights and other significant parameters
like terrain profile (e.g. urban, suburban and rural).
The aim of the present research is to analyse field propagation models like
Hata, Okumura, SUI, Egli, Cost 231, ECC-33, LEE, W-I and Free space field
propagation path loss models for efficient network planning and selection of best fit
model for prediction of exact path loss in the area of NARNAUL (Rajasthan, India).
Further the different climate as summer, winter, fog and rain conditions has been
analysed and attenuation by these climatic conditions be added. At the last the effect of
these climatic conditions has been taken in link budget. The outline of contribution is as
follows:
¾ The field propagation path loss models available in the open literature have been
reviewed.
¾ Field data of BSNL GSM network has been collected over the period of two
years using TEMS navigation tool in different climatic conditions of Narnaul,
Hisar (Haryana, India).
¾ The analysis of Hata, Okumura, COST 231, ECC-33, SUI, Free space, Lee, W-I
and Egli path loss models has been carried out using MATLAB.
¾ The selection of best fit field propagation path loss prediction model for Narnaul
(Haryana, India) region has been estimated on the basis of the path loss and
handoff process.
¾ After analysing the results it has been found that the Okumura path loss model
gives better results as compared to other field propagation path loss prediction
models in Narnaul (Haryana).
¾ The attenuation due to the different climatic conditions has been analysed.
¾ On the basis of performance analysis a suitable development has been done in
Okumura field propagation path loss model by considering the area factor and
climate factor.
¾ The developed Okumura model is proposed for Narnaul (Haryana) in all climatic
conditions.
¾ The field measured data has been compared with the developed Okumura path
loss model in different climatic conditions.
¾ For the validation of the developed Okumura model, a comparative study of
different attenuation has been done with models of Rain attenuation and Fog
attenuation which are given by M. Sridhar et.al. and Altshuler.
¾ For practical validation of the developed Okumura model, a comparative
analysis has been done in Hisar (Haryana, India).
¾ The effect of climatic attenuation on link budget has been analysed and estimate
the coverage area.
21
Based on the results of current research GSM network planner can adopt
field propagation model after analysing their performance. The proper selection of
propagation path loss model provides better coverage area and quality to the customers
[178]. As the climatic conditions also affects on the radio wave propagation. So
developed field propagation path loss model has been implemented to provide more
precise network planning [175].
1.9 OUTLINE OF THESIS
Network planning is a complex process consisting of several steps and
involves through estimation by field propagation models. The purpose of the network
design is the extension of existing network or it has been used for establishment of new
network. The basic requirements for network planning are to meet coverage area and
quality. The environmental factors such as area, foliage, rain, fog etc also greatly affect
network planning. Careful network planning has become important with the rising
development, coverage and congestion of wireless area networks. Many researchers
made effort to analyse the performance of field propagation path loss models for better
network planning. A field propagation model has been estimated for performance
analysis in terms of path loss and handoff. In this thesis analysis has been made to
achieve prediction accuracy of different field propagation path loss models for Narnaul
(Haryana, India). On the basis of this analysis a best fit field propagation model has
been selected and developed on the basis of different climatic conditions. The effect of
different climatic conditions has been analysed and a change in link budget has been
proposed for coverage area.
Chapter 2 discusses radio propagation principles and propagation
mechanisms, primarily for modelling any radio channel in outdoor wireless
communications along with outdoor field propagation path loss models which works on
GSM frequency band.
The chapter 3 describes measurement procedure, hardware, methodology
and logistics of all field propagation campaigns which were conducted. Now a day's
many data collection tools are available in the market in which TEMS data collection
tool has been opted for the for the present research work because it offers many
advantages as compared to other data collection tools. The MATLAB and Mapinfo are
used after the field data collection. The measurement procedure is based on the use of a
drive test system. A drive test simply means drive and test while roaming in the wireless
network in a car. The drive test system provides the insight to the performance of a
network particularly in terms of RF coverage.
In chapter 4, the comparative analysis of different field propagation path
loss models has been carried out with field measured data during drive test in urban
area. On the basis of the analysis it has been conclude that the Okumura path model is
the best fit path loss model for Narnaul (Haryana, India).
22
As the climatic conditions plays very important role in radio wave
propagation. So the climatic attenuations specially rain and fog attenuation has been
taken into account and developed a field propagation model. From the analysis which
carried out in chapter 4, it was observed that the Okumura path loss model is best fit
propagation model for Narnaul (Haryana, India). But still there is some significant error
between measured field data in different climatic conditions and the predicted values by
Okumura model. So to develop more precise model, the effects of Fog and Rain
attenuation has been discussed in chapter 5.
In chapter 6, the cell coverage area and effect on link budget due to different
climatic conditions has been discussed. Further the down link and up link budget
calculations has been done.
The thesis ends with Chapter 7, where conclusions are drawn, and
recommendations made, about the performance of field propagation path loss model
and its suitability in Narnaul (Haryana, India)) on GSM frequency band along with
coverage area and calculations of link budget.
1.10
BENEFITS OF THESIS
The field propagation path loss models are designed for a particular area or
terrain. For example the Okumura model for Urban Areas is a Radio propagation
model that was formulated using the data collected in the city of Tokyo (Japan). The
model is ideal for using in cities with many urban structures but not many big blocking
structures. So when these field propagation path loss models are used in environment
other than where they has been designed, these field propagation models do not predict
good results. The network establishment is very expensive process. If these field
propagation models are used for network planning in any other place where these
models are actually designed, than the correction factor is needed for that particular
area.
This study makes a recommendation on necessary mobile communication
system design. Based on the results of current research GSM network planner can adopt
field propagation model after analysing their network performance. The proper selection
of propagation path loss model provides better coverage area and quality to the users.
As the climatic conditions also affects on the radio wave propagation. So developed
field propagation path loss model can implemented to provide more precise network
planning and calculations of Link budget.
23
CHAPTER 2
FIELD PROPAGATION PATH LOSS MODELS
The theoretical origins of the propagation phenomena and the received
signal power conception are described in this chapter. The basic field propagation path
loss models have been described here.
2.1 BACKGROUND OF FIELD PROPAGATION MODELS
For calculating electromagnetic field strength, different propagation models
have been used for wireless communication network planning during initial
exploitation. The field propagation path loss models are derived by combining
analytical and empirical methods. These models describe the signal attenuation as a
function of distance (between transmitter and receiver), carrier frequency of the signal,
height of antennas and other important parameters like topography profile.
In line-of-sight (LOS) condition, the models such as Harald.T.Friis and free
space model are used to predict the signal power at the receiver end. For initial coverage
deployment, the conventional Okumura model is used in urban, suburban and rural
areas for the frequency range from 200 MHz to 1920 MHz. Hata-Okumura model
which is known as Hata model, a developed version of Okumura model, also
extensively used for the frequency range 150 MHz to 2000 MHz in a build up area.
Comparison of path loss models at different frequencies has been analysed
by many researchers in various respects. In Cambridge (UK) [2], the fixed wireless
access (FWA) network researchers investigated several empirical propagation models in
different terrains as function of antenna height parameters. Another measurement was
taken by considering line of sight (LOS) and at NLOS conditions at Osijek in Croatia in
the year 2007 [118]. Coverage and throughput prediction were considered with respect
to modulation techniques in Belgium [117].
For designing propagation model, different parameters like radiated power,
radiation resistance, received power etc., are very important and those parameters
discussed here.
25
2.2 RADIATED AND RECEIVED POWER
2.2.1 Radiated Power
For a homogeneous, isotropic, linear and lossless dielectric medium, the
magnetic vector potential A is obtained from the Maxwell equations [115], [173]. The
free space wave number is represented as:
'
4
jkR
v
J
A
e
dv
R
P
S
³
(2.1)
here,
is magnetic permeability
J is electric current density
R is Relative distance between source point and field point= |r- r
'
|
k is wave number
=
0
0
= /c
= 2/; ( is wave length of the medium)
Assuming a Hertzian dipole source the current i(t) is written as:
i(t) = Re(Ie
jkR
) (2.2)
Figure 2.1 The Hertzian Dipole
26
Considering figure 2.1 and replacing J dv
'
in equation (2.1) with
zI dz
, A is further
written as:
A =
4
I
R
e
-jkR
v
dz
z (2.3)
so,
^
4
jkr
Il
A
e
z
r
P
S
(2.4)
This expression suggests that the wave is propagating radially in the directions of
having phase constant k. The amplitude of the wave is inversely proportional to the
distance [199]. The magnetic flux density B and magnetic vector potential A are related
as:
B
H
A
P
'u
(2.5)
For spherical coordinate System the ^z is defined as:
z = (cos r - sin ) (2.6)
From equation (2.4) on substituting ^z , now A is further expressed as:
^
^
(cos
sin
)
4
jkr
Il
A
e
r
r
P
T
TT
S
(2.7)
with the help of Maxwell equation the expression for the magnetic field intensity
>
@
1
1
^
sin
1
4
jkr
jkIl
H
A
e
r
jkr
T
I
P
S
§
·
'u
¨
¸
©
¹
(2.8)
For the electric field far away from the dipole, Maxwell's equation for J=0 yields,
E =
1
j
[ × H] (2.9)
Now using equation (2.8), E is further expressed as:
2
2 2
1
1
1
^
^
cos
1
sin
1
2
4
jkr
jkr
Il
jk Il
E
e
r
e
r
jkr
r
jkr
k r
K
K
T
T
T
S
S
§
·
§
·
¨
¸
¨
¸
©
¹
©
¹
(2.10)
where the intrinsic impedance of free space is introduced as:
0
0
P
K
H
(2.11)
Its value is 120 for free space.
For the near field region (kr<<1) the instantaneous Poynting vector which corresponds
to the vector power density (W/m
2
) is given as:
27
w(t) = e(t) × h(t) = Re Ee
jt
× Re He
jt
(2.12)
By writing the real magnetic and electric field as:
h(t) =
1
2
[He
jt
+H
e
-jt
]
(2.13)
e(t) =
1
2
[Ee
jt
+E
e
-jt
]
(2.14)
the instantaneous Poynting vector can be rewritten as:
w(t) = e(t) × h(t) =
1
2
Re [E × H]e
j2t
+ [E × H
] (2.15)
For the near field i.e kr <<1 yields,
< W
r
(t) >=
1
2
Re
-j
|I|
2
I
2
16
2
r
3
0
sin
2
r
= 0
(2.16)
In equation (2.16), the j indicates that the near zone has capacitive
behaviour which means that the dominant field is purely reactive and hence has zero
average power [115], [173].
In the other case, assuming that the observation point is far away from the
dipole (kr>>1), the terms 1/r
2
and 1/r
3
get extremely small. The far field components
then become,
sin
^
4
jkr
jkIl
H
e
r
T
I
S
|
(2.17)
and
sin
^
4
jkr
jk Il
E
e
r
K
T
T
S
(2.18)
From these equations, it is seen that the far field is a spherical wave with H
and E
field which are perpendicular, transverse and propagating in the r direction. In
this case the medium's intrinsic impedance can be written as,
E
H
T
I
K
,
(2.19)
For the far field, time averaged vector power density is
=
1
2
Re
{E × H
} =
1
2
|E
|
2
r
(2.20)
2
2
2
2
2 2
^
( )
sin
32
I l
W t
k
r
r
K
T
S
(W/m
2
).
(2.21)
28
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