Investigating the Best Radio Propagation Model for 4G - WiMAX Networks Deployment in 2530MHz Band in Sub- Saharan Africa

Abstract

One of the salient factors to look at during wireless network planning is developing a modified path loss prediction models to suit a new environment other than the one it was originally designed for. This helps to give accurate predictive outcomes. This paper seeks to demonstrate the effects of applying correction factors on radio propagation model used in planning for 4G-WiMAX network through a comparative analysis between estimated and field data collected on received power for a 4G-WiMAX site. Four existing models were considered for this research; COST 231 Hata, Extended COST 231 Hata, SUI (Stanford University Interim) and Ericsson models. In order to optimize and validate the effectiveness of the proposed models, the mean square error (MSE) and correlation co-efficient were calculated for each model between the predicted and the measured received power for the selected area before and after applying an appropriate correction factor. Based on this, the Extended COST-231 Hata prediction model proved to correlate well with the measured values since it showed least Mean Square Error (MSE) but with highest correlation co-efficient. Through comparative analysis of the corrected models, the Extended COST-231 Hata model could be applied for effective planning of the radio systems in Ghana and the subregion at large.

Full text

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Investigating the Best Radio Propagation Model for 4G -
WiMAX Networks Deployment in 2530MHz Band in Sub-
Saharan Africa
Awal Halifa
Dep’t of Electrical Engineering
Kwame Nkrumah Univ. of
Science and Technology
E. T. Tchao
Dep’t of Computer Engineering
Kwame Nkrumah Univ. of
Science and Technology
J. J. Kponyo
Dep’t of Electrical Engineering
Kwame Nkrumah Univ. of
Science and Technology
ABSTRACT
One of the salient factors to look at during wireless network
planning is developing a modified path loss prediction models
to suit a new environment other than the one it was originally
designed for. This helps to give accurate predictive outcomes.
This paper seeks to demonstrate the effects of applying
correction factors on radio propagation model used in
planning for 4G-WiMAX network through a comparative
analysis between estimated and field data collected on
received power for a 4G-WiMAX site. Four existing models
were considered for this research; COST 231 Hata, Extended
COST 231 Hata, SUI (Stanford University Interim) and
Ericsson models. In order to optimize and validate the
effectiveness of the proposed models, the mean square error
(MSE) and correlation co-efficient were calculated for each
model between the predicted and the measured received
power for the selected area before and after applying an
appropriate correction factor. Based on this, the Extended
COST-231 Hata prediction model proved to correlate well
with the measured values since it showed least Mean Square
Error (MSE) but with highest correlation co-efficient.
Through comparative analysis of the corrected models, the
Extended COST-231 Hata model could be applied for
effective planning of the radio systems in Ghana and the sub-
region at large.
Keywords
4G-WiMAX; Propagation Pathloss Modeling; Sub-Saharan
African Environment; Correlation Co-efficient; Performance;
Field Measurements; Correction Factor.
1. INTRODUCTION
In planning a wireless communication network, it is very
important to consider the predictive tool for a signal loss.
Both predicted and measurement-based propagation tools
reveals that the average RSS decrease logarithmically with
respect to distance be it indoor and outdoor wireless channels.
This is important when estimating the interference, frequency
assignments and evaluation of cell parameters. These are
grouped into three: theoretical, empirical and physical [1]. In
reality, it is difficult coming out with an accurate prediction
model. Practically, researchers that adopt simulation approach
apply empirical models, which depend on fitting curves which
recreates series of measurement values. However, the validity
of an empirical model at a transmission frequency or terrains
rather than the one originally used in deriving that model can
be established through extensive field measurements taken on
a live network. As a result, selecting the best model for a
specific geographical terrain becomes extremely difficult due
to variations in the land-scopes from terrain to terrain. The
validity of the commonly used propagation models therefore
becomes ineffective if they are applied on a terrain rather than
the one originally used in deriving such models. Several
studies conducted in Ghana and some tropical regions have
revealed that a lot of the widely used path loss models have
lower efficiency relative to the field measured values [2].
Hence, this makes it necessary to investigate most appropriate
models that best fits the Ghanaian geographical conditions.
The main objective of this research is to undertake a
performance comparison between simulation results and field
experimental data using the mean square error and correlation
co-efficient analysis before and after applying a correction
factor to propagation models used in planning a deployed 4G-
WiMAX network in the urban centers of Ghana.
2. REVIEW OF RELATED
LITERATURE
Various scholars have made tremendous contributions in line
with path loss model. Zamanillo and Cobo [3] derived a path
loss model for UHF spectrum 4 and 5. It was realized in their
research that for VHF and UHF band, path loss is independent
of frequency, modulation scheme and bandwidth. It was
realized that statistical characteristics of the propagation
channel in the VHF and UHF spectrum could also be
characterized by adopting the model with measurements
obtained on a specified frequency irrespective of the
frequency under consideration. Hanchinal [4] also conducted a
study by making a comparative analysis between the
estimated results of wireless models and field measurement
data. His conclusions were that the COST- 231 and SUI
models give the most suitable predictions for the various
terrain categories specifically for the urban and suburban
terrains. The author further came out with a more corrected
model for estimating the path loss in urban terrains. As a
result of the findings in [4], the corrected path loss model was
derived by comparing between the estimated path loss values
and field measurement values.
Abraham, et al [5] also conducted a study which centered on
comparing the path loss prediction model with field values for
the macro cellular terrain. The findings revealed that, Hata
model gave more accurate and precise path loss predictions
for the macro cellular terrain. Their model produced an MSE
of 2.37 dB which was far less than the minimum acceptable
MSE of 6 dB for a good signal propagation.
All these propagation models as explained in the literature
survey were obtained from the measurement data taken under
European conditions and Asian terrains which aren’t similar
to the peculiar conditions of the Sub-Saharan Environment. It

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29
is possible to adopt the same or similar models for correcting
pathloss models used in planning networks in sub-region by
applying correction factors to these models. Once these terrain
related parameters could be calculated and additional field
measurements taken for validation, the necessary correction
terms could be appropriately applied as required to improve
the accuracy in using these models to predict the received
signal power.
Description of the Measurement Terrain
European climatic condition is classified as been precipitated
or humid unlike the sub-Saharan region which has less
precipitation or completely dry weather conditions which can
affect the performance of wireless propagation models leading
to poor quality of service. In addition, in the sub-Saharan
countries, building structures are not properly sited unlike in
the tropical countries where there are stringent laws and
regulation [6]. In Ghana, it is possible for an operator to site
its Base Transceiver Station to have a clear line of site (LoS)
with neighboring cell sites only for the LoS to be obstructed
by the erection of an unapproved structure which wasn’t
factored during the planning phase of the network systems.
This makes network planning very difficult which leads to
poor quality of service for subscribers.
In order to determine how the peculiar terrain affects
networks deployment, field measurement was carried around
the University of Ghana Campus. This area provides a
measure of the WiMAX network’s radio distance in a typical
urban area. The distribution of Customer Premise equipment
in the study area is shown in Figure 1. The simulation model
adopted for analyzing the capacity and coverage range of the
deployed WiMAX network is based on the behavior of users
and the distribution of CPEs in the cell. The simulation was
done using the stochastic distribution of CPEs within the cell
site as shown in Figure 2. The model adopted the approach
used in [7] which used circular placement of nodes in a
hexagon with one WiMAX Base Station and Subscriber
Stations (SS) which were spaced apart from the Base Station
(BS). The BS are fixed and mobile nodes since mobility has
been configured.
Figure 1: Distribution of CPE in study area
The measurement area was selected due to its urban features
suitable for deploying outdoor wireless networks. The
measurement set-up was composed of a GPS, dongle XCAL-
X, a laptop with a XCAL-X software, WiMAX PCMCIA
CARD and Programmable field strength analyzer. The
measurements were divided into RSS and throughput. The
experimental set-up as shown in Figure 2 was used in testing
real live performance of the WiMAX network under Sub-
Saharan African condition.
Figure 2: Field trial experimental set-up
Several locations within the measurement areas were found to
be fairly obstructed, with a few sections covered by dense
foliage. The RSS test taken in over 16,000 locations within
the cell site. The results of the drive test values have been
summarized in Figure 3.
The highest measured value of -45 dBm was recorded at the
cell centre at about 500m away from the BTS. This value was
fairly good when compared with the simulated cell centre
RSS value of -40dBm. The measured cell edge RSS value was
found to be -100dBm at 4km.
.
Figure 3: Coverage Comparison (-dBm)
The results of the throughput measurement tests have been
summarized in Figure 4. The maximum and minimum field
measured downlink throughputs were 6.1 Mbps and 300kbps
respectively. These values show a large variation when
compared with the simulated maximum and minimum
downlink throughput values of 8.82 Mbps and 1.2 Mbps
respectively.
0
20
40
60
80
100
120
500
4000
R
S
S
I
(
d
B
m
)
Distance
COVERAGE
Simulated
Measured

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Figure 4: Throughput Comparison
Analysis of these field measurement results seem to support
the assumption made in [2] and [8] that the correction factor
for pathloss models which have not been specified for the
Sub-Saharan African environment could have contributed to
the differences between the simulated throughput per sector
and the measured values. Hence there is the need to study how
propagation pathloss affects network deployment in the study
environment.
3. PATH LOSS MODELS EMPERICAL
PROPAGATION MODELS
Generally, the real terrain becomes extremely difficult to
model accurately. Practically, several simulation findings
adopt empirical models, which are derived, based on field
data from different actual terrains on a live network [9]. This
section describes a number of commonly used empirical
models.
Free Space Path loss Models
This model explains the phenomenon where there is a clear
visible transmission path between the transmitting and the
receiving antennas. It is directly proportional to the square of
the distance between the receiving and transmitting antennas,
and to the square of the frequency of the wireless signal as
well [10]. The Free Space Path loss is estimated using the
equation [11][12]:
 
10 10 10
( ) 32.45 10log 20log 20log 1
FSP t
PL dB G f d   
Where:
= Receive (rx) power
=transmit (tx) power
=transmit gain
= Receiver gain
d= antenna separation distance
=free space path loss
COST 231 Hata Path Loss Model.
COST-231 model is the modified form of Okumura-Hata
model for frequencies above 1.8GHz [13]. It has been found
to be appropriate for planning wireless networks in medium
and large cities having a base station antenna height higher
than the surrounding structures. The urban terrain path loss
can be estimated using [14]:
 
50 10 10 10 10
( ) 46.3 33.9*log 13.82*log (44.9 6.55log )*log 2
c te re m
PL urban f h h d C     
For;
=0 dB; medium suburban whiles for Metropolitan it is 3
dB
= frequency band from 150-2000 MHz
= effective tx antenna height within 10-200m range
= effective rx antenna height within 1-10m range
Extended COST 231 Hata Model
Okumura Hata model is mostly applicable in empirical
propagation model, which is based on the Okumura model
[15]. This model is derived for the UHF spectrum. Initial
recommendations of ITU-R P.529 revealed that model was
limited to 3500MHz. The Extended COST 231 is modeled as
[16]:
 
3
fs bm b r
PL A A G G   
For;
=free space attenuation
=Basic median path loss
= transmit antenna height gain factor
= rx antenna gain factor
Stanford University Interim (SUI) Model
Acceptable standards for the spectrum less than 11 GHz have
channel models derived by Stanford University called SUI
models [13]. The SUI models are grouped into: A, B and C
terrains as shown in Table 1. Terrain A has highest path loss
and is suitable for hilly. They are applied to the 3500 MHz
spectrum that is in used in some Countries. Terrain B is used
in areas that are usually flat having a moderate to heavy tree
density or hilly having a light tree density. The SUI model
estimates path loss using: for;
10 0
0
10 log (4)
for d > d
SUI f h
d
PL A X X s
d


    


d: BS - receiving antenna distance[m]
: Reference distance, [100m]
Correction factor for frequencies beyond 2 GHz
: Correction factor for receiver antenna height (m)
s: Correction for shadowing in dB and
: Path loss exponent.
The path loss exponent γ and standard deviation ‘s’ are
selected through statistical analysis. Log-normally distributed
factor 's' denotes shadow fading due to environmental
clutters on the transmission path having values within 8.2 dB -
10.6 dB range [8].
0
2
4
6
8
10
500
4000
T
H
R
O
U
G
H
P
U
T
(
M
b
p
s
)
Distance(m)
Simulated
Measured
r
P
t
P
t
G
r
G
FSP
P
m
C
c
f
te
h
re
h
()
fs
A dB
()
bm
A dB
b
G
r
G
0
d
f
X
h
X


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Table-1: The parameter figures for different terrain of
SUI model
Model
Parameter
Terrain
A
Terrain
B
Terrain C
A
4.6
4
3.6
b(m-1)
0.0075
0.0065
0.005
c(m)
12.6
17.1
20
5
The frequency correction factor and the correction factor for
receiver antenna height for the models are:
For Terrains A & B:
For Terrain C;
Where:
f = frequency (MHz)
is receiver antenna height (meters)
These stated correction factors for this model is applied for
estimating path loss for rural, urban and suburban terrains.
Ericsson Model
In predicting path loss, the network operators sometimes
adopt a path loss estimating tool engineered by Ericsson
Company which is termed as the Ericsson model. This model
depends on the corrected Okumura-Hata model to conform to
variation in parameters relative to the propagation terrain [4].
Path loss estimated by Ericsson model is given as;
Where:
Parameters are;
f: freq.(MHz)
: transmit antenna height (m)
: receive antenna height (m)
4. METHODOLOGY
The methodology implemented in this research is summarized
in Figure 5. With this procedure, one can implement it on a
new environment where the use of correction factor might be
required.
Figure 5: Research Methodology
This research methodology summarizes the procedure used in
determining an appropriate correction factor for the selected
signal prediction model using field data. The received signal
power which is evaluated based on the modified signal
prediction tool is correlated with the received signal power
obtained from the field data. In this adopted research
methodology, the propagation pathloss estimation with the
appropriate simulation parameters and the field data on
received signal strength would be collected. After collecting
data on RSS, analysis would be done with the MSE to
determine appropriate factors of correction. The difference
between the measured and the estimated RSS with the
modified model will be analyzed. The difference in values
between these two is what would be termed as the correction
factor. Once these correction factors for the chosen models
have been estimated, they are then applied to the models and
the received signal powers for each model is then re-
estimated. To get the best suited model, RSS performances
are compared based on two criteria: correlation co-efficient
and mean square error analysis. When this is done the model
having the lowest mean square error but highest correlation
co-efficient is selected as the best and most suitable model.
Hence, these factors would be used to estimate the accurate
received signal strength prediction for the Sub-Saharan terrain
profile.
Estimating the RSS and Mean error
The received signal power, which is estimated as a function of
the respective distance away from the BS can be calculated
using the relation:
Where;
=. Receive power
= Transmit power
= Transmit antenna gain (dBi)
= estimated path loss (dB)
= loss in transmit feeder cable (dB)
= loss due to difference in the T-R antenna polarization
6.0log10 2000
f
Xf


10.8log10 2000
hr
Xh 


20log10 2000
hr
Xh 


r
h
 
   
     
log log log
0 1 10 2 10 3 10
2
*log 3.2 log 11.75 (5)
10 10
PL a a d a h a h
bb
d h g f
r
   




     
2
44.49log 4.78 log
10 10
g f f f


b
h
r
h
 
6P P G PL
rt t tx

    
r
P
t
P
t
G
PL
tx



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Using the received power expression which compensates for
the gain of the receiving antenna gives an accurate prediction
of the receive power and hence in this work, we will estimate
the received power using;
After the received power evaluations have been done, a
comparative analysis based on the criteria for correlation
coefficient, r and mean square error (MSE) will subsequently
be done. In optimizing the pathloss models using the relations
in (1) – (7), the efficiency of the optimized models could be
validated on the basis of the mean square error and correlation
co-efficient using the respective equations:
Where:
:Correlation coefficient,
: measured RSS,
: estimated RSS
: Sample size
From the field measured results summarized in Figure 3, it is
evident that there exists a significant difference between the
field measured data and that of the predicted. As discussed
earlier, these differences in measurement errors is what form
the basis for undertaking this study to correct the models.
After the determination of the mean error parameter, the
correction terms could either be added or subtracted from the
respective path loss equation to give a modified propagation
model which can produce an accurate prediction of signal
power with reference to a Base Station.
From the model optimization methodology shown in Figure 6,
the simulation will be carried out in the following procedures:
i. Path loss from the BTS transmitter on the
University of Ghana Campus will be estimated for
distances relative to the measurements for received
power obtained for the selected models using the
simulation parameters in Table 3.
ii. The received power from the BS transmitter will be
evaluated for distances corresponding to the field
data for each of the selected models.
iii. Correcting factors for the selected model will be
calculated.
iv. Evaluation of RSS of the models having correction
factor is subsequently done.
v. Correlation Coefficient and the MSE between the
predicted and field data on RSS will be estimated to
serve as a basis to modify the respective models.
vi. Comparative analysis between correlation
coefficient and the MSE is done.
5. RESULTS AND DISCUSSION
This section presents the results from the field measurement
study. The preliminary measurements results have been
presented in Table 2. From the results in can be seen that there
is a wide variation been measured and estimated results. This
confirms the primary assumption of this study.
It is a fact that, the major propagation model, Hata model,
which forms the basis for developing the four selected
models, was developed for some propagation scenarios
different from the peculiar Sub-Saharan African terrain. This
could be a contributing factor for the significant difference
that exists between the estimated received power values and
those obtained from measurement on the live network in this
study. This is due to the fact that measurements were
collected in less precipitation regions as in Sub-Saharan
terrain than the original Hata model in tropical regions.
Mean square error and correlation coefficient of the models
using real field values from a live network were used as
criteria for assessing the efficiency of the models as
quantitative measures of accuracy. This finding proved that,
all the selected models equally correlate with the field
measurement data. But in this sense, the criterion used in
selecting the best model is the model that has least errors
(lowest MSE) after applying the correction factors and also
with highest correlation as indicated in Table 4. However,
through comparative analysis based on the calculated figures
the Extended COST-231 Hata Model was considered best
because it has the lowest Mean Square Error of 6.254 dB.
The results in Table 4 serves as a basis for modifying the
extended COST 231 model for the Sub Saharan African
environment as:
This equation can be summarized as:
The correlation coefficients and the MSE between the field
data on received power and the predicted with correction
terms applied to selected models with results tabulated in
Table 4 accordingly.
Comparing the results of MSE's, given in Table 4, it can be
deduced that the new proposed model will be more accurate in
predicting the actual path loss. Based on this, a modified
Extended COST-231 Hata model for the prediction of path
loss for WiMAX networks deployment in the 2500-2530 MHz
bandin urban environment of Greater Accra is developed in
(9b).
This modified model gives a high degree of accuracy and is
able to predict path loss with smaller mean error relative to
the original Extended COST 231 Hata model.
The modified Extended COST 231 Hata model shows greater
performance and higher accuracy than the original Extended
COST-231 Hata model based on the mean square error and
correlation coefficient analysis.
(7)P P G G PL
rr
t t tx

     
 
2
1 (8)
,1 ,1
1
Nem
MSE rr
i
N
   


       
(9)
22
22
me
me rr
rr N
rme
rr
me
rr
NN


  




   

  
  
  
  
  
:r
m
r

e
r

N
 
 
 
 
 
10 10
mod COST-231 Hata
10 10
46.3 33.9log 13.82log
44.9 6.55log log 7.845 9
ified
PL f hb
ah h d c a
mm
b
  
    


 
 
mod COST-231 Hata 7.845 9
ified
PL A A G G b
r
fs bm b
    

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Table 2: Field Data on RSS and Predicted RSS
Distance(m)
Received signal power(dBm)
Measured
RSSI
Estimated using
COST-
231 Hata
Extended
COST-231
Hata
SUI
Ericsson
4200
-73
-97.94
-91.87
-66.86
-100.58
4000
-83
-97.21
-91.14
-65.98
-99.93
3900
-87
-96.84
-90.76
-65.52
-99.6
3800
-92
-96.45
-90.37
-65.05
-99.26
3600
-81
-95.64
-89.56
-64.07
-98.55
3600
-87
-95.64
-89.56
-64.07
-98.55
3500
-79
-95.22
-89.14
-63.56
-98.17
3500
-78
-95.22
-89.14
-63.56
-98.17
3500
-80
-95.22
-89.14
-63.56
-98.17
3300
-77
-94.34
-88.27
-62.5
-97.4
3200
-75
-93.88
-87.81
-61.94
-96.99
3000
-75
-92.92
-86.86
-60.77
-96.14
2800
-73
-91.89
-85.86
-59.52
-95.23
2700
-74
-91.34
-85.33
-58.87
-94.75
2700
-72
-91.34
-85.33
-58.87
-94.75
2000
-71
-86.86
-81.06
-53.43
-90.8
1900
-70
-86.09
-80.34
-52.51
-90.12
1800
-68
-85.28
-79.59
-51.53
-89.41
1600
-67
-83.52
-77.97
-49.39
-87.85
1500
-67
-82.56
-77.09
-48.23
-87
1400
-65
-81.53
-76.15
-46.98
-86.09
1300
-69
-80.42
-75.16
-45.64
-85.12
1200
-70
-79.22
-74.1
-44.19
-84.06
1200
-65
-79.22
-74.1
-44.19
-84.06
1100
-64
-77.92
-72.95
-42.61
-82.91
900
-65
-74.93
-70.35
-38.98
-80.27
800
-63
-73.17
-68.86
-36.85
-78.71
800
-63
-73.17
-68.86
-36.85
-78.71

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700
-60
-71.17
-67.18
-34.43
-76.95
700
-62
-71.17
-67.18
-34.43
-76.95
600
-55
-68.87
-65.29
-31.64
-74.92
570
-61
-68.1
-64.67
-30.71
-74.24
560
-57
-67.84
-64.45
-30.39
-74.01
550
-59
-67.57
-64.24
-30.07
-73.77
530
-57
-67.01
-63.79
-29.4
-73.28
520
-52
-66.73
-63.56
-29.05
-73.03
500
-61
-66.14
-63.1
-28.34
-72.52
500
-58
-66.14
-63.1
-28.34
-72.52
500
-62
-66.14
-63.1
-28.34
-72.52
470
-51
-65.22
-62.36
-27.22
-71.7
450
-59
-64.57
-61.85
-26.44
-71.13
420
-62
-63.54
-61.05
-25.19
-70.22
420
-51
-63.54
-61.05
-25.19
-70.22
415
-50
-63.36
-60.91
-24.97
-70.06
400
-61
-62.81
-60.48
-24.3
-69.57
Table 3: Base Station Simulation Parameters
Base Station Parameter
Value
Transmit Power
30dBm
Transmitter Gain
20dBi
Receiver Gain
18dBi
Loss in transmitter feeder cable
1.2dB
Loss due to variation in the transmitter
receiver antenna polarizations
3dB
Table 4: Propagation Model Performance Before and After Applying
Correction Factors
Before Applying Correction Factor
After Applying Correction Factor
Propagation Models
MSE
Correlation
Coefficient, r
Correction
Factor (CF)
MSE
Correlation
Coefficient, r
Correction
Factor (CF)
COST 231 Hata
181.9705
0.9188
12.5301
24.952
0.9188
12.5301
Extended COST 231 Hata
79.7012
0.9217
7.8451
6.254
0.9217
18.1554
SUI
547.5657
0.9188
-22.3657
47.3428
0.9188
-22.3657
Ericsson
317.2153
0.9188
17.2884
18.3261
0.9188
17.2884

Communications on Applied Electronics (CAE) – ISSN : 2394-4714
Foundation of Computer Science FCS, New York, USA
Volume 7 – No. 7, October 2017 – www.caeaccess.org
35
Figure 6: Simulated Pathloss vrs measured Pathloss
Figure 7: Simulated Received power vrs measured data
Figure 8: Estimation of Received Power (RSS) with
modified models
From the results obtained in Tables 2 and 4, and the optimized
model in (9b), the pathloss and its estimated Received power
simulations have been done and shown in Figures 6 and
Figure 7 respectively. It is evident in Figure 8, that the
estimated RSS with correction factors applied to the models
show that all the models correlates equally with the field
values.
6. CONCLUSION
This research proves that there is absolutely no specific model
which can be used to give consistent results for all
propagation environments due to differences in climatic and
geographic locations. It was further realized that the
characteristics of wireless propagation models differs relative
to the frequency of transmission. This research has revealed
that Extended COST-231 Hata Model is the best wireless
propagation models for WiMAX network deployment in
University of Ghana Campus, Accra within the 2500-2530
MHz band. This will help network operators to accurately
design future WiMAX network with optimum network
throughput with enhanced quality of service to address the
ever-growing demand for wireless services in Accra city and
the sub-region at large. It is recommended that more research
on varying terrain parameters that hinder signal transmission
should be considered to give an optimized model that fits well
with chosen terrain and analyzing the impact varying
frequency bands that proves suitable for chosen terrain.
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