2021 International Conference on Innovative Computing (ICIC)
MEASUREMENTS OF DETERMINISTIC
PROPAGATION MODELS THROUGH FIELD
ASSESSMENTS FOR LONG-TERM
EVALUATION
Zain Waheed, Usman Rauf Kamboh, Muhammad Danish Taqdees, Muhammad Usman, Mehboob Nazim Shehzad
Aroosa Fatima
Department of Computational Sciences
The University of Faisalabad, Pakistan
Email: zain.178.tufians@gmail.com
Abstract—In wireless network planning, a propagation path
loss model is an important tool that enables network planners
to optimize the distribution of the cell towers, and ensure
that service levels are met as predicted. Upon comparison of
different deterministic propagation models, the results show that
the Lognormal shadowing path loss model showed the closest
agreement with a minimum error of base station-1 situated
at officer colony no.1 and base station-2 situated at malikpur
respectively on the calculated path loss. It is noted that the
proposed lognormal shadowing path loss model leads to an
improvement and reduces the minimum error of base station-1
situated at officer colony no.1 in its agreement with measurement
data.
Keywords— PLM, Hata-Okumura, COST-231, ECC-33,
Lognormal Shadowing Model, SUI, Ericsson, G-Net Track
Lite, Cell Mapper, Google Earth Map
I. INTRODUCTION
Since its inception in the early 1980s, wireless communication (WCS) has been a commercial success method that
has displeased the interest of telecommunication engineers
in understanding and estimating the characteristics of signal
transmission in various environments. Wireless technology has
had and will continue to have a significant impact. The future
wireless communication system will lay the groundwork for a
new society in which equipment and people can be linked
at any time and from any place. Because of connectivity
models, wireless access networks have emerged as a critical
tool in managing communications, especially at home and at
work. Propagation path loss models (PLMs) are widely used
for performing feasibility studies during the early stages of
designing the communication system. To estimate path loss,
a variety of prediction PLMs are available. Prediction PLMs
play a critical role in signal coverage optimization, inter- ference detection, and effective network resource utilization [1],
[7]. Wireless communication systems are one of the growing
systems nowadays. Each year the demanding data for mobile
communications is growing. The number of subscribers is
increasing exponentially while network providers are bound to
provide cheap and reliable services due to market competition.
978-1-6654-0091-6/21/$31.00 © 2021 IEEE
The demand for large bandwidth is a huge impediment for
wireless service providers in such a competitive environment.
Nowadays Femto base stations have been deployed in houses,
offices, bus stations, airports, malls, etc. due to their high capacity, better coverage, low cost, and high spectrum efficiency
[13], [19].
II. BACKGROUND
Generally, propagation PLMs have focused on estimating
the RSS at a distance from the BTS. In WCS, sufficient
understanding is required to use appropriate PLMs among
various PLMs. Cellular communication systems are a form of
cellular communication system, accurate propagation path loss
models help us to identify the locations where new cell sites
are required for providing the network coverage and provide
acceptable cost estimation. On the other hand, incorrect path
loss estimation will either reduce system efficiency or increase
system cost WCS’s efficiency is determined by the channel’s
characteristics. The transmitting approach is influenced by the
channel’s characteristics. In order to optimize RF coverage,
PLMs and RSS are necessary. Since terrain conditions differ
from place to place, there are no widely agreed PLMs. In
order to minimize the effects of interference, it is necessary
to accurately estimate the channel characteristics [2]–[4]. The
primary goal of a 3G and 4G LTE network is to achieve
optimum coverage and efficiency. Significant performance
factors including handover, RSS, call success rate, and dropped
call ratio all play a key role in achieving any mobile communication system’s quality goals show in Fig. 1. The comparison
of propagation PLMs with field measurement data for different
areas deterministic path loss models. Comparative research has
been conducted in several countries, including Banta (Algeria), the South-South part of Nigeria (India), Kuala Lumpur
(Malaysia), and others, as will be addressed in the literature
study. To our knowledge, no such research was conducted
in any Pakistan region that analyses contact connection loss
while considering PLE behavior. A critical review of the study
accompanies the literature study [2], [5], [6].
A. Hata-Okumura Path loss Model
The Hata-Okumura model is a well-known empirical PLM that
predicts RSS and is based on the Okumura PLM [9], [10].
The Hata-Okumura model is an Ultra High-Frequency model
that has been well-developed (UHF). UHF has a frequency
range of 300 MHz to 3 GHz. Previously, the International
Telecommunication Union (ITU) created this framework for
further extension up to 3.5 GHz based on its recommendations
(ITU-R). The Okumura model provides no information at
frequencies greater than 3 GHz. The Hata-Okumura Path loss
Model equations for different areas show in Table I.
TABLE I
PATHLOSS E Q U A T I O N S F O R D I F F E R E N T A R E A S . [9]
Area
Urban
Suburban
Rural
Fig. 1. Propagation Path loss Mechanisms.
Path loss Equation (dB)
A + Blog (d) - E
A + Blog (d) - C
A + Blog (d) - D
III. PROBLEM STATEMENT
Several studies are comparing PLMs with field measurements in different countries, as discussed in the literature
survey, but we have not been able to locate any systematic
research on 4G LTE path loss modeling for any region of
Pakistan. As a result, this study aims to compare the output
of the most widely used predictive PLMs with field assessments at three different locations along the Canal Expressway
Faisalabad in Pakistan. All previous measurements were done
with complicated software and expensive equipment like a
spectrum analyzer, a laptop with Ericsson software, a network
communication analyzer (ACTIX analyzer 4.05) software, and
a GPS receiver, among other things. In this research work
an easy and inexpensive way to calculate the field data is
provided. A Net-Track Lite and Cell mapper applications are
used during the site surveys.
where, f is the frequency in MHz, d represents the distance
between BTS and MTS in meters, hb is the height of BTS
antenna in meters, hm is the height of mobile receiver antenna
in meters, and a(hm) is the correction parameter in dB. The
effective height of MTS antenna a(hm) is defined in as:
a (hm) = [1.1 × log(4)–0.7] × hm–[1.56 × log(f )–0.8] (1)
B. COST-231 Hata path loss Model
COST-231 is an abbreviation for Cost-of-Production-231.
The Hata PLM model is a cross between the F.Ikegami and
the J.Walfish models. The COST-231 Hata model offered
correction factors for suburban, rural, and urban locations
show in table II. The path loss equation for this model is
as follows show in Equation 2 [15].
TABLE II
IV. PROPOGATION MODELS
WCS uses electromagnetic radiation to transmit data from the
transmitter to the receiver. Path loss is caused by the
interaction of area and electromagnetic waves, which
decreases signal power.
• It can be difficult to describe any random scenario using
a mathematical model at times. The estimated behavior
is then observed using random data. An empirical model
is made up of algorithms and various mathematical equations to perform the signal propagation [8].
• The deterministic models employ various laws to direct
electromagnetic signal propagation to locate the RSS
precisely. Deterministic models usually need a complete
3D map of the propagation region [8].
• Stochastic models are used to represent various conditions as a set of random variables. While stochastic
models need less information about the environment, they
are the least accurate. Empirical and stochastic models are
used to predict propagation path loss at the frequency of
3.5GHz. [8].
PARAMETER A N D
CORRECTION FACTORS FOR DIFFERENT AREAS
[ 16]
Area
Urban
Suburban
Rural
Correction Factor cm
3dB
0dB
15dB
Parameter a(hm)
3.2 × (log(11.75 × hr))4.97
(1.1 × (0.7))hr (1.56 × (0.8)
(1.1 × (0.7))hr (1.56 × (0.8)
PL (dB) = 46.3+33.9×log10 (f) −13.82×log10 (hb) −a (hm)
+ (44.9 − 6.55 × log10 (hm) × log10 (d) + cm (2)
Where, f denotes the frequency in MHz, hb is the height of
BTS antenna in meters, hr is the height of MTS antenna in
meters, cm represents the correction parameter and its value
is defined according to area.
C. Lognormal Shadowing path loss Model
The lognormal distribution is the result of random shadowing across a large number of measurement locations with
the same T-R spacing but variable amounts of noise on the
propagation path. This phenomenon is known as lognormal
Shadowing. In the lognormal shadowing model, an
Equation is used to represent the variations in the measured
data showin Equation 3.
d
(3)
PL (d) = PL (d0) + 10nlog10 ( ) + Xσ,
d0
Where, n represents the PLE. The value of PLE relies on the
type of area. Xσ represents a random variable with zero mean
and standard deviation of σ.
TABLE IV
ERICSSON PLM CONSTANTS [ 17]
Environment
Urban
Suburban
Rural
k0
36.2
43.2
45.95
k1
30.2
68.93
100.6
k2
12.0
12.0
12.0
k3
0.1
0.1
0.1
V. COMPARISON WITH MEASUREMENT
D. Stanford University Interim path loss Model
The SUI model was created by the Institute of Electrical
and Electronics Engineers as part of the IEEE 802.16 wireless connection working group (IEEE). The transmitter and
receiver antenna heights in the SUI model extend from 10 to
80 meters and 2 to 10 meters, respectively [14]. Terrain A, B,
and C are the three types of terrain identified by this model
show in Table III.
Comparative analysis between measured and estimated values
was required to verify the efficiency of predictive PLMs, and
field measurements were required for comparison. As a result,
selecting BTSs is critical for obtaining field measurements.
The field measurements were taken on the Canal Expressway
in Faisalabad, Pakistan. This Expressway runs from east to
west and provides easy access between Faisalabad and other
cities show in Fig 2.
TABLE III
PARAMETER V A L U E S O F D I F F E R EN T T Y P E S O F T E R R A I N S . [12]
Parameters of SUI Model
a
b
c
Terrain A
4.6
0.0075
12.6
Terrain B
4.0
0.0065
17.1
Terrain C
3.6
0.005
20
The basic path loss equation with the SUI model’s correction
parameters is given as:
Where, d0 denotes the reference distance in meters, Xf is the
correction parameter for frequency over 1.5GHz, Xh is the
correction parameter for height of receiver antenna in meters,
s represents the shadowing correction parameter in dB, and γ
Denotes the PLE. The factors n, A, Xf, and Xh are presented
as:
d
PL = A + 10nlog10 ( ) + Xf + Xh + sford > d0 (4)
d0
E. ECC-33 path loss Model
The Electronics Communication Committee (ECC) recommended the ECC-33 PLM. Essentially, it is extrapolated from
the Okumura model’s measurements, and Okumura’s assumption is updated in this model [18]. The ECC-33 PLM is an
empirical model which consists of four terms and it can be
defined as show in Equation 5.
PL (dB) = Abm + Afs − Gt − Gr
(5)
F. Ericson path loss Model
The Ericsson model was created by network planning engineers using tools given by the Ericsson organization to predict
Path loss. The Ericson PLM is based on an improved version
of the Hata-Okumura PLM for various types of propagation
areas Path loss can be estimated using this model as show in
Equation 6 and different areas constants show in Table IV.
Fig. 2. Selected base stations on Faisalabad Canal Expressway.
To obtain field measurements on the Canal Expressway,
three BTSs were chosen as signal sources. These BTSs were
located in Officer Colony no. 1, Malikpur, and Canal Garden. The terrains chosen have less vegetation and houses or
structures that are often under 20 meters show in Fig 3 and
information of BTSs show in Table V.
PL = k0+k1+log10 (d) +k2log10 (hb) +k3log10 (hb): log10
(d) — 3 : 2[log10(11 : 75hr)2]+44 : 49log10(f ) − 478[log10(f )]2
(6)
TABLE V
INFORMATION O F S E L E C T E D
STATIONS.
Base Station
BTS1
BTS2
BTS3
Location
Officer Colony
Malikpur
Canal Garden
BASE
Coordinates(Lat,Long)
31.423863, 73.117926
31.451816, 73.135970
31.458844, 73.169961
Cell-Id
FSD 130047
FSD 137213
FSD 132436
Fig. 3. Image of BTS1 , BTS2 , BTS3
A. Base Stations Parameters
In Matlab, these parameters were used to analyze the field
measurements. The parameters for each BTS, such as transmission frequency, transmitted power, antenna height, EIRP,
and BTS antenna gain, were obtained from the “Mobil ink”
headquarters in Kashmir Bridge Faisalabad show in Table
VI.
TABLE VII
COMPARISON O F
Base Station
BTS1
TABLE VI
PARAMETERS O F
3.36% and 2.86%, respectively. As a result, the Hata-Okumura
and Lognormal shadowing PLMs, among others, are the best
at estimating calculated path loss. The Hata-Okumura and
Lognormal shadowing methods have the lowest error rates of
3.36% and 2.86%, respectively. As a result, the Hata-Okumura
and Lognormal shadowing PLMs, among others, are the best at
estimating calculated path loss. Since the locations used in this
study are in a metropolitan area but not a densely populated
place, ECC-33 does not provide a good estimate of path
loss. The Hata-Okumura and Lognormal shadowing PLMs,
on the other hand, were the nearest to field measurements,
while the SUI PLM underestimated the measured path loss.
Furthermore, with an error of 9.47%, the COST-231 PLM
indicates a minor deviation from field measurements show in
Table VII.
PERCENTAGE DIFFERENCE FOR BTS1.
Hata-Okumura
3.36%
COST-231
9.47%
Lognormal
2.86%
Ericsson
19.23%
SUI
13.00%
ECC-33
34.7%
SELECTED BASE STATIONS.
Parameters
BTS Transmitting power
MTS antenna height
BTS antenna height
BTS antenna gain
MTS antenna gain
Operating frequency
EIRP
Values
47 Watts
1.5m
60m
15dBi
1dBi
1.8 GHz
62dBm
B. Equipment and Software Used
The hardware used to collect the data was an Oppo F5
smartphone. The field measurements were taken using the
different applications. The G-net track application for Android
mobile devices is a wireless monitor and drive test tool.
Without the use of specialized instruments, this application
allows for the monitoring and recording of mobile network
parameters. A cell mapper is a tool for determining cellular
coverage. We can use this software to verify the coverage of
our mobile network provider. On the map, we can also see
our provider’s cell tower positions. The Google Earth Map is a
three-dimensional representation of the earth based on satellite
imagery. This can be used to calculate exact distances between
various locations shown in figure 2.
Fig. 4. Comparison of estimated and field measured path loss for BTS1.
VI. RESULT AND DISCUSSION
A. Resemblance of path loss Models with Field
Assessmentsfor BTS1
The driving test in the BTS1 coverage area produced a mean
path loss of 139 dB as a result of the results. The COST-231,
Hata-Okumura, Lognormal shadowing, Ericsson, SUI, and
ECC- 33 PLMs measure mean path loss values of 127.6183
dB, 136.3034 dB, 144.9947 dB, 168.6057 dB, 122.4747
dB, and 191.4006 dB, respectively. The Hata-Okumura and
Lognormal shadowing methods have the lowest error rates of
B. Resemblance of
Assessmentsfor BTS2
path
loss
Models
with
Field
The driving test in the BTS2 coverage area revealed a mean
path loss of 136.55 dB. The COST- 231, Hata-Okumura,
Lognormal Shadowing, Ericsson, SUI, and ECC-33 PLMs
estimate mean path loss values of 118.3578 dB, 126.9482 dB,
133.6706 dB, 160.1459 dB, 112.3564 dB, and 215.1509 dB,
Respectively. The Lognormal shadowing PLM, with a marginal
Error of 2.17%, best estimates the measured path loss among
the PLMs used. This means that, as compared to other PLMs,
the lognormal shadowing PLM better estimates the calculated
path loss. According to the findings, the ECC-33 and Ericson
PLMs have a relatively high estimate of calculated path loss.
The ECC-33 PLM is far from the measured path loss, suggesting that it overestimates the path loss. Furthermore, the SUI
PLM revealed a path loss of 126.2257 dB. With an error of
7.26%, the Hata-Okumura model reveals the slight deviation
from field measurements show in Table VIII.
lognormal and Ericson PLMs are closer to measured path loss
from distances up to 1.4 km to 2 km show in Table IX.
TABLE IX
COMPARISON O F
Base Station
BTS3
Hata-Okumura
9.59%
PERCENTAGE DIFFERENCE FOR BTS3.
COST-231
15.64%
Lognormal
13.06%
Ericsson
13.70%
SUI
19.78%
ECC-33
99.10%
TABLE VIII
COMPARISON O F
Base Station
BTS2
PERCENTAGE DIFFERENCE FOR BTS2.
Hata-Okumura
7.26%
COST-231
13.49%
Lognormal
2.17%
Ericsson
16.76%
SUI
17.78%
ECC-33
56.52%
Fig. 6. Comparison of estimated and field measured path loss for BTS3.
VII. CONCLUSION AND FUTURE WORK
Fig. 5. Comparison of estimated and field measured path loss for BTS2.
C. Resemblance of
Assessmentsfor BTS3
path
loss
Models
with
Field
A driving test resulted in a mean path loss of 138.55 dB when
performed in the BTS3’s area coverage. The mean path loss
values expected by the model are 118.3578 dB, 126.9482dB,
158.6706 dB, 160.1459 dB, 112.3564 dB, 284.3259 dB.
PLMs from COST-231, Hata-Okumura, Lognormal
shadowing, Ericsson, SUI, and ECC-33. With a minimum
error of 9.59%, the Hata-Okumura PLM had the best
agreement with the calculated path loss. As a result, the
Hata-Okumura PLM, among other PLMs, is the best at
estimating the calculated path loss. The ECC-33 PLM
showed a quite high estimation of measured path loss of
295.1104 dB. Furthermore, the Hata- Okumura, SUI, and
COST-231 PLMs are closer to measured path loss from
distances up to 0.3 km to 1 km, while the
This work is concluded in the following paragraphs after
reviewing the findings obtained by simulation of both comparative analyses of PLMs and multi-slope PLMs. When comparing calculated path loss to theoretical values based on
percentage difference, the lognormal shadowing PLM had the
best agreement, with a minimum error of 2.86% and 2.17%
for BTS1 and BTS2, respectively. The Hata- Okumura PLM
has the best estimate of the calculated path loss in BTS3,
with an error of 9.59%. With a maximum error of 34%, 56%,
and 99%, respectively, the ECC-33 PLM overestimates the
calculated path loss of BTS1, BTS2, and BTS3. As a result,
the ECC-33 model cannot be recommended for the selected
BTSs’ coverage area. The work performed in this thesis serves
as the basis for several future initiatives. The collected field
measurements can be used to investigate the path loss effect
in various applications that depend on RSS and PLE, such
as RSS-based localization. Different path loss models may be
added to the multi-slope model in the future. Furthermore,
path loss models can be applied by estimating path loss using
various machine learning techniques.
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