International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
Implementation of Cellular Propagation Models in
Diverse Environments
Deepak Katiyar#1 , Vikas Mittal*2
#1
M.Tech. Student, Dept. of ECE, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India.
*2
Asst. Prof., Dept. of ECE, Maharishi Markandeshwar University, Mullana, Ambala, Haryana, India.
Abstract— This paper presents the implementation and analysis
of different propagation models in cellular communication
system under different environmental conditions. The paper
discusses the various factors which influence the propagation of
wireless signal in free space thereby causing the degradation of
signal quality. The propagation models implemented are: free
space path loss, Okumura model, COST 231 Hata model, SUI,
ECC-33models and Ericsson model. These models are simulated
with respect to standard parameters for Cellular transmission.
The results analysed were stated for each model in respective
environmental locations. The results show that the multipath and
Non-Line-Of-Sight scenarios in urban area, all models
experiences higher propagation losses in urban area compare to
suburban and rural areas.
Keywords— Propagation models, cellular communication system,
free space path loss, Okumura model, COST 231 Hata model,
SUI, ECC-33models and Ericsson model.
I. INTRODUCTION
Modern wireless communication technology has
revolutionized the field of telecommunication. Today, the
wireless networks have far succeeded in complimenting the
traditional communication networks in aspects of system
capacity, network connectivity, signal strength and power
usage [1]. Inspite of the reliability of wireless communication
technology, implementation of mobile communication is
burdened with certain propagation complications. Over the
last decade, a number of wireless propagation models have
emerged on the basis of different user requirement and
environmental conditions like as geographical location factor,
user density, vegetation density, network applicability, data
rate, fading and other path losses etc [2]. The environmental
factors greatly impact the propagation mechanisms of wireless
signals through the free space, causing subsequent loss in
signal power. Thus, the characteristic features of wireless
communication networks are often evaluated on the basis of
applied propagation model. The implementations of various
cellular propagation models in communication networks for
respective wireless services are geographically selective.
Therefore, the environmental factors play an important role in
estimation of signal strength, interference optimization,
network design and estimation of number of users allocated
per cell [3].
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II. FACTORS AFFECTING SIGNAL PROPAGATION
The parameters for the signal transmission should be
selected in a manner so that it maximizes the quality of
service for the user and minimizes the use of system resources.
A number of algorithms are being employed or investigated to
optimize the signal transmission process [4]. Traditional
cellular networks are primarily focused on voice applications
and consequently there are fewer worries associated with
degradation of signal quality, poor data rate or poor signal
strength. Traditional algorithms employ simple intuitive rules
to compare the received signal strength from different points
of connection and then decide the best algorithm suitable for
transmission [5]. Degradation of the signal level, however, is a
major point of consideration, and the mechanisms followed by
traditional algorithms degrade the service provided by the
network. Consequently, more complex algorithms are needed
to be employed for the optimal propagation of signal in free
space. To select an appropriate algorithm, a comparative
performance evaluation of a variety of algorithms is required.
However, the comparative performance evaluation of different
propagation algorithms is an extremely challenging subject.
Performance criteria for selecting the optimum propagation
algorithm are affected by the type of network. The
performance criteria for cellular phone networks such as GSM,
UMTS and CDMA2000, rate adaptive wireless data networks
such as WLAN and WPANs, and inter-technology
heterogeneous networks are different [2]. For this purpose, the
received signal strength (RSS) at base station is calculated to
quantify the signal quality as well as calculate the path
loss.The rate adaptive and heterogeneous data networks are
focused on optimizing the delivered average throughput to the
user. However, this average throughput is affected by different
factors in a heterogeneous and in a homogeneous multi-rate
network. Typical factors include free-space loss, refraction,
diffraction, reflection, aperture-medium coupling loss, and
absorption. The propagation of signal from wireless networks
across heterogeneous space is also influenced by terrain
contours, environment (urban or rural, vegetation and foliage),
propagation medium (dry or moist air), the distance between
the transmitter and the receiver, and the height and location of
antennas. Signal propagation can be affected by many factors,
as mentioned in Table I.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
TABLE I
FACTORS CAUSING THE PROPAGATION LOSS
Factors (Propagation Loss)
Free Space Loss
Absorption
Scattering
Reflection
Description
Free space loss describes the loss that occurs as a signal travels through space with
no other attenuation caused by outside influences. This occurs because the signal
spreads out as the distance from the transmitter increases.
Absorption is a loss that occurs if the signal passes through varying mediums or
obstacles in which some of the transmitted signal is converted into another form of
energy, usually thermal, and some of it continues to propagate. Any material or
atmospheric condition that is non-transparent to electromagnetic signals will result
in absorption of the transmitted signal. The conversion of energy occurs at the
molecular level, resulting from the interaction of the energy of the radio wave and
the material of medium or obstacle.
Scattering is a condition that occurs when a radio wave encounters small
disturbances of a medium, which alters the direction of signal. Scattering is difficult
to predict because of the random nature of the medium or objects that causes it.
Reflection occurs when a radio wave approaches the boundary of two mediums and
redirects back into the original medium in a different direction, rather than
permeating through into the new medium.
Refraction
Refraction occurs when a radio wave passes from one medium to another with
different refractive indices resulting in a change of velocity within an
electromagnetic wave that results in a change of direction.
Diffraction
Diffraction losses occur when there is an obstacle in the path of the radio wave
transmission and the radio waves either bend around an object or spread as they
pass through an opening. Diffraction can cause great levels of attenuation at high
frequencies. However, at low frequencies, diffraction actually extends the range of
the radio transmission.
Polarization is used in an electromagnetic wave to describe the direction of the
electric field vector. Fading is a radio wave variation in signal strength, caused by a
change in the polarization of the transmitted radio wave. Fading can result from
reflection, refraction, or absorption. It is a significant problem because antennas are
designed to receive a radio wave in a certain polarization, and when the polarization
of signal is changed, the receiving antenna is incapable of receiving the polarization
changes.
Polarization Fading
Multipath Fading
Multipath fading refers to the fading that occurs as a result of the multiple paths that
a signal ends up taking between the transmitter and receiver. Because of the varying
arrival times of the signals from the various paths, the signals may or may not be in
phase with each other. If the radio waves are received in phase, they combine to
form a stronger signal. If the radio waves are out of phase, a weaker signal is
produced. Multipath fading is the primary concern in the urban environment.
Terrain
The terrain over which a signal propagates accounts for a great deal of the loss it
experiences along its path. As expected, mountainous terrain can significantly
degrade or completely block a signal, but the composition of the terrain can also
cause attenuation, especially at low altitudes. Radio waves tend to travel better over
more conductive mediums such as water, but encounter more attenuation travelling
over areas of dirt or sand.
Vegetation
Just like the terrain, vegetation can impact the transmission of radio waves. Solid
trees can cause significant attenuation, but even leaves can cause scattering of a
signal.
Buildings
Buildings and other man-made structures can cause losses due to all of the above
factors, and are by far the main source of attenuation in an urban environment. This
study is designed to attempt the difficult task of accurately accounting for the losses
caused by these structures.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
III. CELLULAR PROPAGATION MODELS
A wireless communication terminal needs to connect to
multiple points of connection and perhaps multiple networks
as it moves from one location to another. The method of using
different networks with the same terminal for inter-network
mobile communications is often referred to as intertechnology, heterogeneous, or non-homogeneous networking.
That’s why the system applications of a wireless network are
dependent on propagation models that describe the attenuation
of the transmitted signal as a function of the distance between
network terminals. Propagation models are classified as
deterministic, empirical and semi-empirical [6]. Deterministic
models make use of the physical laws governing radio wave
propagation mechanisms to predict transmission loss at a
particular location. The models involve detailed and accurate
description of all the objects in the propagating channel.
Empirical model is based on statistical characterization of the
received signal extensive measurements conducted with
respect to several different parameters. It requires less
computation effort unlike deterministic model that is site
specific. A semi-empirical or semi-deterministic model
combines the analytical formulation of physical phenomena
with statistical fitting of variables by adjustment using
experimental measurements. Hence, the choice of propagation
model plays an important role in the performance of a cellular
network. The standard propagation models are implemented in
different environments like suburban area scattered with trees,
houses and urban areas with large building and houses or
village with close houses and tall trees. These propagation
models are described below:
where, PLOM : Median path loss [dB], Lf : Free space path
loss [dB], Amn (f,d): Median attenuation relative to free space
[dB], G(hte): Base station antenna height gain factor [dB],
G(hre): Mobile station antenna height gain factor [dB], GAREA:
Gain due to the type of environment [dB] and parameters, f:
Frequency [MHz], hte: Transmitter antenna height [m], hre:
Receiver antenna height [m] and d: Distance between
transmitter and receiver antenna [km]. Figure I provides the
values of Amn (f,d) and GAREA.
Attenuation and gain terms in this model are given as:
20 log10 (hte/200) for 1000 > hre > 10m
G(hte) =
10 log10 (hre/3)
for hre ≤ 3m
20 log10 (hre/3) for 10m > hre > 3m
where, G(hte): Base station antenna height gain factor [dB], hte:
Transmitter antenna height and hre: Receiver antenna height.
A. Free Space Path Loss Model (FS)
Free space model defines the strength of the signal lost
during propagation from transmitter to receiver. The free
space path loss calculation is done using the following
equation:
PLFS = Gt – Gr + 32.44 + 20 log(d) + 20log(f)
2.1
where Gt and Gr are transmitted and received antenna gains in
dB; d is T-R separation in Km and f is frequency in MHz [7].
B.
Okumura Model
The Okumura model is a well known classical empirical
model to measure the radio signal strength in build up areas.
The model was built by the collected data in Tokyo city in
Japan. This model is perfect for using in the cities having
dense and tall structure, like Tokyo. In this model, while
dealing with areas, the urban area is sub-grouped as big cities
and the medium city or normal built cities. Okumura model
can predict path loss in urban, suburban and rural area up to 3
GHz and the median path loss model can be expressed as [8]:
PLOM = Lf + Amn (f,d) – G(hte) – G(hre) – GAREA
2.2
FIGURE I: MEDIAN ATTENUATION AND AREA GAIN FACTOR [9]
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2.3
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
C. COST 231 Hata Model
The Hata model was introduced as a mathematical
expression to mitigate the best fit of the graphical data
provided by the classical Okumura model [10]. Hata model is
used for the frequency range of 150 MHz to 1500 MHz to
predict the median path loss for the distance d from
transmitter to receiver antenna up to 20 km, and transmitter
antenna height is considered 30 m to 200 m and receiver
antenna height is 1 m to 10 m. To predict the path loss in the
frequency range 1500 MHz to 2000 MHz, the COST 231 Hata
model was initiated as an extension of Hata model. It is used
to calculate path loss in three different environments like
urban, suburban and rural (flat). The basic path loss equation
for this COST-231 Hata Model can be expressed as [11]:
PLCOST = 46.3 + 33.9log10(f) – 13.82log10(hb) – ahm +
(44.9 - 6.55log10(hb))log10(d) + cm
2.4
where d: Distance between transmitter and receiver antenna
[km], f: Frequency [MHz], hb: Transmitter antenna height [m].
The parameter cm has different values for different
environments like 0 dB for suburban and 3 dB for urban areas
and the remaining parameter ahm is defined in urban areas as:
ahm = 3.20 (log10 (11.75))2 – 4.70, for f > 400MHz
where, the parameters are d: Distance between BS and
receiving antenna [m], Xf: Correction for frequency above 2
GHz [MHz], Xh: Correction for receiving antenna height [m],
S: Correction for shadowing [dB] and Y: Path loss exponent
The random variables are taken through a statistical
procedure as the path loss exponent Y and the weak fading
standard deviation s is defined. The log normally distributed
factor S, for shadow fading because of trees or other clutter on
a propagations path and is between 8.2 dB and 10.6 dB [11].
The parameters A and path loss exponent Y are defined as [12]:
where, the parameter hb is the base station antenna height in
meters. This is between 10 m and 80 m. The constants a, b,
and c depend upon the types of terrain, given in Table II. The
value of parameter Y = 2 is for free space propagation in an
urban area, 3 < Y < 5 for urban NLOS environment, and Y > 5
for indoor propagation [12].
TABLE II
PARAMETER VALUES OF DIFFERENT TERRAIN FOR SUI MODEL
Model Parameter
a
2.5
The value for ahm in suburban and rural (flat) areas is given as:
Terrain C
4.0
3.6
b (m )
0.0075
0.0065
0.005
c (m)
12.6
17.1
20
The frequency correction factor Xf and the correction for
receiver antenna height Xh for the model are expressed as:
Stanford University Interim (SUI) Model
X f = 6.0 log 10 (f/2000)
The IEEE 802.16 Broadband Wireless Access working
group proposed the standards for the frequency band below 11
GHz, containing the channel model developed by Stanford
University, namely the SUI models. This prediction model
came from the extension of Hata model with frequency larger
than 1900 MHz. The correction parameters are allowed to
extend this model up to 3.5 GHz band. In the USA, this model
is defined for the Multipoint Microwave Distribution System
(MMDS) for the frequency band from 2.5 GHz to 2.7 GHz
[11]. The base station antenna height of SUI model can be
used from 10 m to 80 m. Receiver antenna height is from 2 m
to 10 m. The cell radius is from 0.1 km to 8 km [12]. The SUI
model describes three types of terrain: A, B and C. Terrain A
can be used for hilly areas with moderate or very dense
vegetation. This terrain presents the highest path loss. Terrain
B is characterized for the hilly terrains with rare vegetation, or
flat terrains with moderate or heavy tree densities. This is the
intermediate path loss scheme. Terrain C is suitable for flat
terrains or rural with light vegetation, here path loss is
minimum. The basic path loss expression of the SUI model
with correction factors is presented as:
PLSUI = A + 10ylog10 (d/d0) + Xf +Xh + s for d > d0
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Terrain B
2.6
where, hr is the receiver antenna height in metres.
D.
Terrain A
4.6
-1
ahm = 1.11log10 (f-0.7)hr – ( 1.5log 10f – 0.8)
2.8
A = 20log 10 (4πd0/λ) and Y = a – bhb + (c/hb)
2.7
2.9
-10 log 10 ( hr / 2000) for terrain type A and B
2.10
X h=
-20 log 10 ( hr / 2000) for terrain type C
where, f is the operating frequency in MHz, and hr is the
receiver antenna height in meter. For the above correction
factors, this model is extensively used for the path loss
prediction of all three types of terrain in rural, urban and
suburban environments.
E.
Hata-Okumura extended model or ECC-33 Model
One of the most extensively used empirical propagation
models is the Hata-Okumura model [9], which is based on the
Okumura model. This model is a well-established model for
the Ultra High Frequency (UHF) band. Recently, through the
ITU-R
Recommendation
P.529,
the
International
Telecommunication Union (ITU) encouraged this model for
further extension up to 3.5 GHz [13]. The original Okumura
model does not provide any data greater than 3 GHz. Based on
prior knowledge of Okumura model, an extrapolated method
is applied to predict the model for higher frequency greater
than 3 GHz. In this model path loss is given by [11]:
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
PL HO = Afs + Abm - Gb - Gr
2.11
where Afs: Free space attenuation [dB], Abm: Basic median
path loss [dB], Gb: Transmitter antenna height gain factor and
Gr: Receiver antenna height gain factor. These factors can be
separately described and given as:
Afs = 9.24 + 20log10 (d) + 20 log10 (f)
2.12
Abm=20.41+9.83log10 (d)+7.894log10 (f)+9.56[log 10 (f)]2 2.13
G b = log10(hb/2000){13.958 + 5.8[log10(d)]2}
TABLE IV
SIMULATION PARAMETERS
Parameters
Values
Base station transmitter power
43 dBm
Mobile transmitter power
30 dBm
Transmitter antenna height
30 m in urban and suburban
2.14
When dealing with gain for medium cities, the Gr will be
expressed in [11]:
2.15
Gr = [42.57 + 13.7log10(f)][log10 (hr) – 0.585]
For large city:
2.16
Gr = 0.759(hr) – 1.862
where, d: Distance between transmitter and receiver antenna
[km], f: Frequency [GHz], hb: Transmitter antenna height [m]
and hr: Receiver antenna height [m]. This model is the
hierarchy of Okumura-Hata model. So the urban area is also
subdivided into large city and medium sized city, as the model
was formed in the Tokyo city having crowded and tallest
buildings.
F.
IV. SIMULATION PARAMETERS
2.11 in the model are
The simulation parameters applied
presened in Table IV below:
Ericsson Model
This model is developed by Ericsson also stands on the
modified Okumura-Hata model to allow room for changing in
parameters according to the propagation environment. Path
loss according to this model is given by [12]:
and 20 m in rural area
Receiver antenna height
6m
Operating frequency
3.5 GHz
Distance between Tx-Rx
5 km
Building to building distance
50 m
Average building height
15 m
Street width
25 m
Street orientation angle
300 in urban and 400 in
suburban
Correction for shadowing
8.2 dB in suburban and
10.6 dB in urban area
V. RESULTS
In this study 3 different environments i.e. urban, suburban
and rural are taken. The height of receiver antenna is 6 m and
transmitter antenna height is 30 m in urban and suburban and
20 m in rural area. The simulated results for different models
in urban, suburban and rural environments are shown in the
Figure II, III and IV respectively.
2.17
PLEM = a0 + a1.log10 (d) + a2 .log10(hb) + a3.log10(hb).
log10(d) - 3.2(log1o(11.7hr)2) + g(f)
and g(f) = 44.49log10(f) – 4.78(log10(f))2:
2.18
where parameters f: Frequency [MHz], hb: Transmission
antenna height [m], hr: Receiver antenna height [m] and
default values of these parameters (a0, a1, a2 and a3) for
different terrain are given in Table III.
TABLE IIII
VALUES OF PARAMETERS FOR ERICSSON MODEL.
a0
a1
a2
a3
Urban
36.2
30.2
12.0
0.1
Suburban
43.20
68.93
12.0
0.1
Rural
45.95
100.6
12.0
0.1
Environment
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FIGURE II: SIMULATED RESULTS IN URBAN ENVIRONMENT
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International Journal of Engineering Trends and Technology (IJETT) – Volume 15 Number 1 – Sep 2014
get LOS signal, in that situation, the COST-Hata model
showed the less propagation loss compare to SUI model and
Ericsson model. Therefore, for the worst case scenario for
deploying a coverage area, the maximum coverage can be
served by using more transmission power, but it will increase
the probability of interference with the adjacent area with the
same frequency blocks. On the other hand, if the less
propagation models are considered for deploying a cellular
region, it may be inadequate to serve the whole coverage area.
Some users may be out of signal in the operating cell
especially during mobile condition. So, there needs to be a
trade-off between transmission power and adjacent frequency
blocks interference while choosing a propagation model for
initial deployment.
REFERENCES
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FIGURE III: SIMULATED RESULTS IN SUBURBAN ENVIRONMENT
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[5]
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[10]
FIGURE IV: SIMULATED R ESULTS IN R URAL ENVIRONMENT
[11]
VI. CONCLUSION
From the comparative analysis made in this paper, it is
found that due to multipath and NLOS environment in urban
area, all models experiences higher propagation losses
compare to suburban and rural areas. From the results, it is
observed that there is no single model that can be
recommended for all environments. In urban area, the
Ericsson model showed the lowest propagation loss as
compared to other models. Alternatively, the ECC-33 model
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signal probability is high, in that case, the LOS calculation
may be considered. Alternatively, if there is less probability to
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