Development of a modified propagation model of a wireless mobile communication system in a 4G network

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6489~6500
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6489-6500  6489
Journal homepage: http://ijece.iaescore.com
Development of a modified propagation model of a wireless
mobile communication system in a 4G network
Akande Akinyinka Olukunle1
, Akinde Olusola Kunle2
, Odeyinka Oluwadara Joel2
,
Ilori Abolaji Okikiade2
, Adigun Matthew Olusegun3
, Ajagbe Sunday Adeola4
1
Department of Electrical and Electronic Engineering, Faculty of Engineering, Federal University of Technology, Owerri, Nigeria
2
Department of Electrical and Biomedical Engineering, Faculty of Engineering and Technology, First Technical University,
Ibadan, Nigeria
3
Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology,
Cape Town, South Africa
4
Department of Computer Engineering, Faculty of Engineering and Technology, First Technical University, Ibadan, Nigeria
Article Info ABSTRACT
Article history:
Received Nov 11, 2022
Revised Feb 8, 2023
Accepted Mar 9, 2023
Pathloss is a key element that causes signal deterioration in the channel as the
signal power reduces inversely with propagation distance, this deterioration
experienced by the channel is majorly as a result of reflection, absorption, and
scattering of the signal. This study however takes into consideration the radio
path loss for precise base station (BS), frequency, and power adjustment
prediction evaluated over a frequency of 2.3 GHz. With a distance range
between 0.1 and 1.5 km for collection of data on the measured received signal
strength (MRSS), five empirical models and a modified model were used to
validate the measured data to determine their suitability for pathloss prediction
at Federal University of Technology, Owerri (FUTO), Imo state, Nigeria. The
results shows that the root mean square error (RMSE) for the Okumura-Hata,
COST 231-Hata, Ericsson model, Lee, Stanford University Interim (SUI),
ECC-33, and modified models are 14.33, 9.73, 25.79, 48.4, 33.76, and
8.31 dB respectively. Additionally, the Ericsson model provided 0.498 dB,
the COST 231-Hata recorded 0.733 dB, and the modified model provided
0.453 dB for mean absolute percentage error (MAPE). Therefore, the
improved model produces the best results, consequently, be deployed to
approximately predict path loss for mobile radio coverage in Owerri, Nigeria.
Keywords:
Communication
Frequency
Model
Modified
Pathloss
This is an open access article under the CC BY-SA license.
Corresponding Author:
Akinde Olusola Kunle
Department of Electrical and Biomedical Engineering, Faculty of Engineering and Technology, First
Technical University
Ibadan, Nigeria
Email: Olusola.akinde@tech-u.edu.ng
1. INTRODUCTION
Deploying wireless mobile communication networks require a critical review of pathloss prediction
technologies. It is critical because cellular mobile communication signal losses differ from one location to
another. Therefore, cellular mobile system advancements and their future applications necessitate a
comprehensive site location plan, a good line of sight along the channel and a high data transfer rate. For high
mobility applications like mobile access, the peak data transfer rate of a 4G cellular mobile system is anticipated
to be around 100 Mbit/s. The 4G network’s quick data transfer has the ability to give users of the network
access to a wide range of services. Electromagnetic waves are used in wireless communication networks to
transport data between a transmitter and a receiver [1]. Due to obstructions in the channel caused by high

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6489-6500
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buildings, dense vegetation, sharp edges, and hilltops, this transmitted signal is therefore vulnerable to
attenuation, resulting in reflection, diffraction, scattering, and absorption of the signal [2].
Several factors have been identified to have significant effects on the received signal strength (RSS),
some of which are the environment in which the cellular network is placed, the operating frequency, and the
radius of the cell. Additionally, pathloss has been discovered to vary directly with separation between the
transmitter and the receiver [3]. The qualities of the received radio signal are influenced, in brief, by three
fundamental occurrences. There are several of these, including losses in the radio wave path, multiple path
(small-scale) fading, and large-scale shadow fading. Pathloss can therefore be referred to as signal attenuation
in the communication channel as it travels from source to base station (BS) [2]. A crucial stage in building up
a wireless communication system is path loss prediction in mobile radio channels. It is necessary to develop
exact mathematical tools for adjusting power and radio characteristics that will ensure adequate network
coverage of a given area [4]. A number of models that can be used in a system of mobile radio communication
have been reported in the literature.
Several models, which were created for a specific terrain, suffer when used in another terrain [5], [6].
The performance of various path loss models was also shown to be poor when compared to the observed data
in several investigations carried out in Nigeria and other tropical countries [7]. As a result, it is necessary to
assess the situation and choose a model that is appropriate for the Federal University of Technology, Owerri
(FUTO) environment. This study uses data from a drive test to examine the performance of the Okumura-Hata,
Stanford University Interim (SUI), ECC-33 model with measured data in the South-East suburbs of Nigeria,
near FUTO. An updated shadowing empirical model was also created based on the current models, and the
path loss in (dB) was compared with the information from the drive test.
2. LITERATURE REVIEW
Recent research on path loss prediction models has emphasized the need to critically assess the
environment at hand before deploying the best model for that environment. In evaluating the model with
optimum performance, several studies have been done in various places. The predictions of the SUI,
COST-231, and ECC-33 models were contrasted with the path-loss data acquired at Cambridge at a frequency
of 3.5 GHz, Roslee and Kwan [8]. The result in the chosen area was overestimated by SUI and COST-231.
The ECC-33 model provided the closest fit and was thus suggested for use in urban settings.
By utilizing a least-squares method, Halifa et al. [9] optimized the Hata model for Malaysian
sub-urban area to produce a more precise prediction. Frequency measurements were taken outside between
400 and 1,800 MHz, Hata model produced the best results. The optimized model was deployed, verified in a
different but similar environment to identify the relative error for evaluating the Hata model’s effectiveness.
A minute mean relative error was gotten which indicates a successful optimization.
In a similar manner, Ghana’s worldwide interoperability for microwave access (WiMAX) network in
the 2,500 to 2,530 MHz frequency was the focus of the work presented Gadze et al. [10]. A WiMAX site near
the University of Ghana was used as a focus for measurement. Four empirical models were considered
applicable for predictions and they were compared with the measured data. COST-231 extended model, had a
greater correlation coefficient and the lowest root mean square error (RMSE). Therefore, being the most
appropriate for the measured data, it was suggested in Ghana and the sub-regions for effective radio network
planning. Another study was conducted to address the difficulties in radio transmission that network
professionals face in choosing the most accurate and appropriate propagation model for Ghana. Faruk et al.
[11] evaluated a wide range of long-term evolution (LTE) path-loss metrics for frequencies of 800 MHz and
2,600 MHz in urban and suburban locations. When the scientists compared the data to six widely used
propagation models, it was found that the enhanced versions of the Ericson, ECC-33, and SUI models produced
in the study accurately predicted the path-loss.
In Nigeria, measurement validation was presented using the modified Hata model for pathloss
evaluation at 1.8 GHz in a rural environment of the Niger Delta region [12]. A modified pathloss model was
developed to predict signal strength received at a reasonable accuracy. A mean prediction error value that was
less than 10.4 dB and a standard deviation error value less than 18 dB was achieved for the network considered
in the study area. Another path loss variation was also studied in the South-South region at 876 MHz, the loss
increased by 35.5 and 25.7 dB/decade in the urban and Sub-urban regions respectively [13]. The modified Hata
model was recommended to be applicable for path loss prediction in the area. The shortfall of the study was
that it does not consider rural areas as a part of the coverage.
Five experimental models were taken into account while examining the performance of path loss for
LTE network Emeruwa and Iwuji [14]. The actual field data on the network was captured at a frequency of
700 MHz using drive test. The forecast employing test mobile system (TEMS) research and discovery network
planning tools provided the best prediction with the Ericsson model among the models used.

Int J Elec & Comp Eng ISSN: 2088-8708 
Development of a modified propagation model of a wireless mobile … (Akande Akinyinka Olukunle)
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An optimized cost 231 model was developed Sharma et al. [15] to estimate path-loss in Jaipur, India
over a 4G wireless communication link operating at 1,800 MHz. The measurement supports the distinction
between anticipated and measured path-loss using the measured field strength as basis. Findings in the study
revealed that COST-231 model is best for the studied environment. There also exists a mean error (ME) of
-5 dB between measured values and the COST-231 model.
A tangent activation function and 48 hidden neurons were implemented in the feedforward neural
networks (FNNs) algorithm [16] to create an ideal model. On eleven distinct base stations, each running at
1,800 MHz frequency, drive test readings were frequently taken. When the effectiveness of the suggested
model was evaluated using common measures, little prediction error was discovered.
A signal attenuation prediction model for global system for mobile (GSM) operation at frequency of
1,800 MHz for several GSM networks at the chosen location of University of Nigeria Campus was proposed
in [17]. At that frequency, measurements were taken in the field depending on the signal’s strength.
Regression analysis was used to assess field measurements in order to create the desired model. Based on its
least error value, the proposed attenuation model outperformed other existing models in comparison.
Ibhaze et al. [18] used data gathered in a few Nigerian urban areas to develop new models while
examining the efficacy of heuristic, empirical, and geographical methodologies. To compare the derived
models to the empirical models, data were measured. It was noted that the ECC-33 and Egli models failed
to produce RMSE values that were acceptable. The authors’ work suggests that empirical models be
improved in the future for the best accurate prediction. The authors suggested combining empirical and
heuristic models for prediction in order to reduce the inaccuracy that is frequently associated with empirical
models.
Imoize et al. [19] created a 2,100 MHz version of the Ericson model for the Alagbado neighborhood
of Lagos, Nigeria. A sophisticated polynomial was fitted for data measurement. The measurement was
correlated with various existing empirical models. There was a lesser likelihood of error in the prediction
employed in the analyzed wireless channel, even if the model had previously been applied in the prediction of
lower range frequencies different from the frequency spectrum considered.
Path-loss models for a 4G LTE network in urban and suburban areas of Lagos were described in [20].
At 3.4 GHz, the reference signal received power (RSRP) was noticed. The obtained models were connected to
the observed data. The COST 231-Hata and Ericson models performed remarkably well in the chosen settings.
The least square regression algorithm was used by the authors to create the model that performed the best. The
generated models provided a good probability result with RMSES of 6.20 and 5.90 dB in the urban and
sub-urban area that was chosen. When the results of propagation measurements in comparable surroundings
were compared, it was found that the models would accurately represent radio coverage, increasing the value
of mobile services in the same terrain.
The design of LTE networks in respect to the performance metrics it gives is crucial given the rising
demand for cellular communication in terms of throughputs and adequate connectivity. In order to improve the
performance of cellular networks for voice communication as well as data transmission, several studies have
been reviewed to find propagation models that accurately give forecast of path-loss in continents throughout
the world. However, the successful operation of current propagation models in wireless environments other
than the locations where they were first intended to be deployed may present a different ideal. However,
numerous studies show that when modified to the data collected from the experimental location, many widely
used path-loss models perform better. This means that in order to understand the peculiarities of the
environment and the elements that may cause signal losses along the course of radio propagation, a critical
study and in-depth investigation of a specific terrain must be conducted. The wireless network planner will
then be able to design or adapt an appropriate propagation model for a specific environment by thoroughly
evaluating the elements that can result in signal loss.
A consideration for different terrains such as Rural, Suburban and urban were implemented in the
research carried out in [21]–[23]. Other studies carried out considered special measuring field such as the study
by [24] which focused on vegetation area and pathloss prediction for indoor environment by [25]. Braga et al.
[26] focuses on a mixed city-river area and made it clear that only few studies have been carried out on mixed
city-river area,
In the study, the development of a modified shadowing empirical model for the study area was carried
out by modification of the log distance shadowing model, the derivative of the path loss exponent obtained and
the standard deviation about a mean value substituted in the log distance shadowing model established the
modified path loss model for FUTO University environment. Performance analysis based on the model’s
accuracy was carried out by utilizing statistical performance metrics like RMSE and mean absolute percentage
error (MAPE). The amount of variation and error in the measured data determines how effective the proposed
model is.

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2.1. Empirical propagation models
There are theoretical empirical propagation models for predicting path loss over various topographies
and specific environmental conditions. However, this study covers the location-specific and the generally
employed empirical path loss model to predict the attenuation of the signal power from the mobile radio station
to the BS transceiver. Okumura-Hata model is a globally acceptable model and had been adjudged as a good
model to benchmark new approaches by the International Telecommunications Union [7]. Its only limitation
is that it does not incorporate tropospheric parameters.
2.1.1. Okumura-Hata model
This model is appropriate for propagation in metropolitan areas [27]. The model’s frequency range is
restricted to 150 to 1,500 MHz, and BS antenna height, hbt, varies depending on the topography from 30 m to
200 m. According to [28], [29], the typical calculation for median path loss in metropolitan areas is as (1).
𝑃𝐿𝑃(𝑢𝑟𝑏𝑎𝑛)𝑑𝐵 = 69.55 + 26.16 𝑙𝑜𝑔 𝑓𝑐 − 13.82 𝑙𝑜𝑔 ℎ𝑏𝑡 − 𝑎(ℎ𝑚𝑡𝑠)
+[44.9 − 6.55 𝑙𝑜𝑔 ℎ𝑏𝑡]𝑙𝑜𝑔 𝑑 (1)
𝑓𝑐 represent the carrier frequency (in MHz) and d is distance (km) between BS and mobile station, ℎ𝑏𝑡 and ℎ𝑚𝑡𝑠
are the height (in meters) of base station antenna and mobile antenna respectively, 𝑎(ℎ𝑚𝑡𝑠) is the correction
factor for active mobile antenna height. The mobile antenna correction factor for major cities is written as (2)
and (3).
For 𝑓𝑐 𝑙𝑒𝑠𝑠 𝑡ℎ𝑎𝑛 300 𝑀𝐻𝑧, 𝑎(ℎ𝑚𝑡𝑠) = 8.29 [𝑙𝑜𝑔(1.54ℎ𝑚𝑡𝑠)]2
− 1.1 𝑑𝐵 (2)
For 𝑓𝑐 𝑔𝑟𝑒𝑎𝑡𝑒𝑟 𝑜𝑟 𝑒𝑞𝑢𝑎𝑙𝑠 𝑡𝑜 300 𝑀𝐻𝑍, 𝑎(ℎ𝑚𝑏𝑠) = 3.201 [𝑙𝑜𝑔(11.75ℎ𝑚𝑡𝑠)]2
− 4.97 𝑑𝐵 (3)
The mobile antenna correction factor 𝑎(ℎ𝑚𝑡𝑠) for small; medium size city is (4).
𝑎(ℎ𝑚𝑡𝑠) = 1.11[𝑙𝑜𝑔(𝑓
𝑐) − 0.7]ℎ𝑚𝑡𝑠 − [1.56 𝑙𝑜𝑔(𝑓
𝑐) − 0.8]𝑑𝐵 (4)
The standard Okumura-Hata formulation for path loss calculation in suburban area [30], [31] is expressed in (5).
𝑃𝐿𝑃(𝑠𝑢𝑏𝑢𝑟𝑎𝑛) (𝑑𝐵) = 𝐿𝑃(𝑢𝑟𝑏𝑎𝑛)𝑑𝐵 − 2[𝑙𝑜𝑔
𝑓𝑐
28
]2
− 5.4 (5)
2.1.2. COST 231-Hata model
The European Co-operative for Scientific and Technical research created and developed the
COST-231-Hata model (EURO-COST). The model is limited to a frequency between of 1,500 and 2,000 MHz,
30 to 200 m for base station antenna height, and separations of 1 to 20 km between the transmitter antenna and
receiving antenna [32] provides the formula for the model as (6).
𝑃𝐿𝑃(𝐶𝑂𝑆𝑇) 𝑑𝐵 = 46.3 + 33.9 𝑙𝑜𝑔 𝑓𝑐 − 13.82 𝑙𝑜𝑔 ℎ𝑏𝑡 − 𝑎(ℎ𝑚𝑡𝑠)
+[44.9 − 6.55 𝑙𝑜𝑔 ℎ𝑚𝑎ℎ] 𝑙𝑜𝑔 𝑑 + 𝐶𝐴𝐶𝐹 (6)
𝐶𝐴𝐶𝐹 defines the correction factor of the area, 𝑃𝐿𝑃 is path loss in (dB), 𝑓𝑐 is the carrier frequency (in MHz),
ℎ𝑏𝑡 𝑖 is the height (in meters) of BS antenna, ℎ𝑚𝑎ℎ is height (in meters) of mobile antenna, 𝑎(ℎ𝑚𝑡𝑠) is
the correction factor for effective mobile antenna height, 𝑑 is distance (km) between base station and mobile
station. 𝐶𝐴𝐶𝐹 = 0 dB for average sized environs and sub-urban environ, and 3 dB for urban areas. The mobile
antenna correction factor for a large city is expressed for frequency less than or equal to 300 Mhz in (7).
𝑎(ℎ𝑚𝑏) = 8.29 [𝑙𝑜𝑔(1.54ℎ𝑚𝑏)]2
− 1.1 𝑑𝐵 (7)
while for a small or medium sized city, it is given as (8).
𝑎(ℎ𝑚𝑏) = 1.1[𝑙𝑜𝑔(𝑓𝑐) − 0.7]ℎ𝑚 − [1.56 𝑙𝑜𝑔(𝑓𝑐) − 0.8] (8)
2.1.3. Ericsson 9999 model
Ericsson model [33] was established on modified model of Okumura-Hata thereby giving opportunity
for variation in parameters relative to propagation arena. The model is utilized up to frequency value of
1,900 MHz. Path-loss based on this model is estimated by [34] as (9) and (10).

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𝐿𝑃 (𝐸𝑅𝐼𝐶)(𝑑𝐵) = 𝑎0 + 𝑎1 𝑙𝑜𝑔(𝑑) + 𝑎2 𝑙𝑜𝑔 ℎ𝑏
+[𝑎3 𝑙𝑜𝑔(ℎ𝑏𝑠ℎ) 𝑙𝑜𝑔(𝑑)] − 3.2 [𝑙𝑜𝑔(11.75 ℎ𝑟ℎ)2] + 𝑔(𝑓𝑐) (9)
𝑔(𝑓𝑐) = 44.49 𝑙𝑜𝑔(𝑓) − 4.78[𝑙𝑜𝑔(𝑓)2
] (10)
𝑓 is frequency in MHz, ℎ𝑏𝑠ℎ is height in (m) of transmitter antenna, ℎ𝑟ℎ is receiver antenna height (m). Each
of a0, a1, a2, and a3 has default standards for diverse environment [35]
2.1.4. Lee model
This model was established for use in United State of America at 900 MHz [28]. With additional field
calibration measurement (drive tests), Lee model parameters can easily be adapted to the local surroundings.
The model can be adapted to the remote environment easily compared to other path loss models. 𝐿0, depends
on the environment.
𝐿𝑃 (𝐿𝑒𝑒)(𝑑𝐵) = 𝐿0 + 𝛽 𝑙𝑜𝑔(𝑑) + 10 𝑛 𝑙𝑜𝑔 (
𝑓
𝑓0
) − 𝑍0 (11)
𝐿0 is reference median path loss, 𝑓0 is the benchmark frequency, 𝑛 is the exponent variations with the
frequency, 𝑑 is the distance in km, 𝛽 is the slope of the path loss curve, 𝑍0 is the correction factor. The value
of n and 𝑦 are based on empirical data given as (12).
𝑛 = {
2 𝑓𝑜𝑟 𝑓𝑐 < 450 𝑀𝐻𝑧 𝑎𝑛𝑑 𝑖𝑛 𝑠𝑢𝑏𝑢𝑟𝑏𝑎𝑛/𝑂𝑝𝑒𝑛 𝑎𝑟𝑒𝑎
3 𝑓𝑜𝑟 𝑓𝑐 > 450 𝑀𝐻𝑧 𝑎𝑛𝑑 𝑖𝑛 𝑈𝑟𝑏𝑎𝑛 𝑎𝑟𝑒𝑎
𝑦 = {
1 𝑓𝑜𝑟 ℎ𝑚 < 3 𝑚
2 𝑓𝑜𝑟 ℎ𝑚 < 10 𝑚
(12)
2.1.5. SUI model
This is a recognized model for IEEE 802.16 by Stanford University [33]. The Hata model extension
with correction parameters for frequencies beyond 1,900 MHz served as the foundation for this model. This
model may be extended up to a 3.5 GHz frequency band using the SUI model with correction parameter [36].
Terrain A is ideal for a hilly landscape with moderate to heavy foliage densities and reflects a densely populated
area. Ground B is an example of a suburban setting, with generally flat terrain and moderate to dense tree cover.
Landscape C, or a rural setting, refers to flat terrain with moderate tree coverage [36].
𝐿𝑃 (𝑆𝑈𝐼)(𝑑𝐵) = 𝐹𝑆 + 10 𝑣 𝑙𝑜𝑔 (
𝑑
𝑑0
) + 𝑋𝐶𝑓 + 𝑋𝑏𝑠ℎ + 𝑆𝑠𝑓 𝑓𝑜𝑟 𝑑 > 𝑑0 (13)
𝐿𝑃 (𝑆𝑈𝐼) is path loss in dB, A is free space path loss, 𝑑 is the distance from the transmitter to the receiver, 𝑑0 is
the reference distance, 𝑋𝑓𝑐𝑓 is the frequency correction factor above 2 GHz (in MHz), 𝑋𝑏𝑠ℎ is correction factor
base station height (m), 𝑆𝑠𝑓 is correction for shadowing (dB), v is path propagation exponent. The value of
parameter 𝑣 = 2 is for free space propagation in an urban area, 3 < 𝑣 < 5 is for urban non-line-of-sight
(NLOS) environment and 𝑣 > 5 is for indoor propagation.
The range of parameters involved are base station (transmitter) antenna height is (10 to 80 m), mobile
station antenna height between 2 to 10 m, Cell Radius of (0.1 to 8 km). The log normally distributed
factor (𝑆𝑠𝑓), for shadow fading because of trees and other clutter on mobile radio path and its value is between
8.2 and 10.6 dB [36].
The free space path loss (FS) is given as (14).
FS = 20 log (
4πd0
λ
) (14)
where d0 is the distance between transmitter and receiver, and 𝜆 is the wavelength. The path loss exponent v is
given by (15).
v = a − bhb + (
c
hbsh
) (15)
The parameter ℎ𝑏𝑠ℎ is approximately 10 to 80 meters for the height of the base station antenna. The
constants: 𝑎, 𝑏(𝑚−1
), and 𝑐(𝑚) rely on terrain-specific characteristics.

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2.1.6. ECC-33 models
ECC-33 model, extrapolated from the actual measurements by Okumura and modified its assumptions
to obtain similar values on models. The path loss model [31], [37] is given as (20).
𝐿𝑃(𝐸𝐶𝐶−33)(𝑑𝐵) = 𝐴𝐹𝑆𝐴 + 𝐴𝑀𝑃𝐿 − 𝐺𝑡𝑥 − 𝐺𝑟𝑥 (20)
𝐴𝐹𝑆𝐴 is free space attenuation, 𝐴𝑀𝑃𝐿 is basic median path loss, 𝐺𝑡𝑥 represents transmitter antenna height gain
factor, 𝐺𝑟𝑥 is the gain factor of the receiving antenna height. Each of those specifications in (20) is defined by
[31] as in (21) to (23).
𝐴𝐹𝑆𝐴 = 92.4 + 20 𝑙𝑜𝑔(𝑑) + 20 𝑙𝑜𝑔(𝑓) (21)
𝐴𝑀𝑃𝐿 = 20.41 + 9.83 𝑙𝑜𝑔(𝑑) + 7.894 𝑙𝑜𝑔(𝑓) + 9.56 [𝑙𝑜𝑔(𝑓)]2
(22)
𝐺𝑡𝑥 = 𝑙𝑜𝑔 (
ℎ𝑏𝑠𝑡
200
) [13.98 + 5.8(𝑙𝑜𝑔 𝑑)2
] (23)
For medium sized cities
Grx = [42.57 + 13.7 log (f) ] [ log (hmst) − 0.585] (24)
for large cities
Grx = 0.759 (hr) − 1.862 (25)
where, d is the distance between base station and mobile station in (kilometer), ℎ𝑏𝑠𝑡 is the height of transmitter
antenna in meters, ℎ𝑚𝑠𝑡 is the height of mobile antenna in meters.
2.1.7. Log-distance shadowing model
An empirical method for constructing a mobile radio propagation model based on analytical equations
that replicate a measured data set is the log-distance shadowing model. Signal propagation in terrestrial wireless
communication is characterized by a number of characteristics, including path loss, shadowing, and fading.
The weakening of signal power as it travels from the base station to the mobile station is known as path loss.
The average received signal power in a mobile radio channel drops logarithmically with distance, according to
experimental and theoretical propagation models [30]. As a result, [38] provides the path loss model as a
function of distance.
LP (di)α (
d
d0
)v
(26)
LP (di) = LP (do) (
d
d0
)v
(27)
LP (di) = LP (do) + 10vlog(
d
d0
) (28)
where, v is path loss exponent, d is the distance that exists between the BS and cellular mobile stations, and
𝑑0 is the reference distance.
v =
LP (di)−LP (do)
10log(
d
d0
)
(29)
In balancing the effect of random shadowing due to clutter, the modified power path loss model [30] can be
written as (30).
LP (di) = LP (do) + 10vlog (
d
d0
) + βσ (30)
𝛽𝜎 defines the zero-mean gaussian distributed random variable in (dB) and standard deviation σ in (dB). Using
regression analysis in a mean square approach, path loss exponent v, can be determined as (31).

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v =
∑ [LP (do)−LP (di)]2
N
i=1
∑ 10 log (di d0
⁄ )
N
i=1
(31)
𝐿𝑃 (𝑑𝑜) is the path loss measured and 𝐿𝑃 (𝑑𝑖) defines the path-loss predicted at any distance 𝑑𝑖, 𝑁 represents the
number of data points. The standard deviation is expressed as (32).
σ = (
1
N
∑ [LP (do) − LP (di)]2
N
i=1 )
1
2 (32)
The expression 𝐿𝑃 (𝑑𝑜) − 𝐿𝑃 (𝑑𝑖) gives an error term relative to 𝑣, from (31) and the summation of the
MSE, 𝑒(𝑣) is defined as (33).
e(v) = ∑ [LP (do) − LP (di)]2
N
i=1 (33)
The derivative of (33) can be equated to zero while the mean square error is placed at minimal value to solve
for 𝑣.
∂e(v)
∂e
= 0 (34)
3. METHOD
Using laptop-installed TEMS 11.0 research software, the received signal power was measured on a
network of base transceiver stations located inside the Federal University of Technology, Owerri campus. The
receiving antenna was mounted via the open rooftop of the Toyota Camry car and the height of the antenna
from the ground was measured to be 1.5 m. A google earth software was also connected live to guide in the
drive test path. The Sony Ericsson W995 TEMS phone, a global positioning system (GPS) unit, an HP Compaq
laptop, and a drive test vehicle make up the drive test equipment as shown in Figure 1.
Figure 1. Experimental setup
The driving test path was originally planned for measurements and is motor-capable. The cabin of the
driving car is outfitted with every piece of equipment as it should be. The Ericsson handset was used to measure
the signal strength that was received during a brief call and upload it to the laptop’s TEMS log file. Every
100 m, the following data were recorded: voice signal, coordinates, RSS, call loss, and call establishment. The
GPS receiver provides the (longitude and latitude) coordinates. Additionally, the GPS offers the route taken
and a Google map of the area. The base station for those experiments has an antennae height of 35 m operates
at a frequency of 2.3 GHz while the height of the mobile station was chosen to be 1.5 m.
It was possible to forecast the path loss as measured on the FUTO University Campus with the aid of
the empirical models discussed earlier. The models were chosen based on their ability to forecast mean route
loss as a function of many characteristics, including operational frequency, mobile antenna heights, and
distance. Table 1 shows the results of the regression analysis between the measured data and the anticipated
path-loss data. The path loss exponent (v) is calculated by equating the derivatives of (33) to zero as:

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e(v) = 1139.919n2
− 5857.738n + 9259.05 = 0
∂e(v)
∂e
= 2(1139.919n) − 5857.738 = 0
v = 2.57
Thus, from (32), the standard deviation, σ (dB), about a mean value, can be estimated as:
σ = (
1
15
(1139.919(2.57)2
− 5857.738(2.57) + 9259.05 )
1
2
σ = 10. 75 dB
By substituting for 𝐿𝑃(𝑑0)𝑅𝑒𝑓, 𝑣 and adding σ to compensate for the error into (30), this will lead to the
development of a modified shadowing empirical model for the investigation area. Therefore, the modified path
loss model for Owerri FUTO University Campus suburban environment is presented as (34).
LPR(di) = 107.2 + 10(2.57) log (
d1…N
dref
) + 10.75 d B (34)
Table 1. Depicts FUTO University Campus regression analysis
Distance (m) RSS (dBm) Measured PL 𝑳𝒑(𝒅𝟎) 𝐝𝐁 Predicted PL 𝑳𝒑(𝒅𝒊) 𝐝𝐁
100 -76.21 107.2 107.2
200 -80.44 128.0 107.2+3.01n
300 -93.36 124.4 107.2+4.77n
400 -99.21 134.2 107.2+6.02n
500 -107.3 138.0 107.2+6.99n
600 -100.3 132.3 107.2+7.78n
700 -89.45 120.4 107.2+8.45n
800 -107.3 138.3 107.2+9.03n
900 -95.21 126.2 107.2+9.54n
1,000 -100.2 148.0 107.2+10.00n
1,100 -104.0 131.0 107.2+10.41n
1,200 -98.33 133.8 107.2+10.79n
1,300 -101.2 132.3 107.2+11.14n
1,400 -95.89 126.9 107.2+11.46n
1,500 -106.0 135.1 107.2+11.76n
4. RESULTS AND DISCUSSION
The measured path loss, the modified path loss model five other existing models: Okumura-Hata, Cost
231-Hata, Ericsson 999, Lee, SUI and ECC-33 model were examined at regular intervals of 0.1 km and the
results were recorded and presented in Table 2. Figure 2 compares the measured Path loss and some existing
empirical models considered in this work.
Table 2. The path loss measured, existing models and modified model for FUTO University suburban
environment
Distance
(km)
Measured
PL (dB)
Okumura-
Hata (dB)
Cost231-
Hata (dB)
Ericsson
999 (dB)
Lee
(dB)
SUI
(dB)
ECC-
33 (dB)
Modified
model (dB)
0.1 107.2 87.96 102.85 74.80 78.89 121.19 332.16 117.95
0.2 128.0 98.43 113.33 105.13 80.05 137.2 338.89 125.69
0.3 124.4 104.6 119.45 122.87 80.73 146.57 343.2 130.21
0.4 134.2 108.9 123.8 135.46 81.21 153.21 346.42 133.42
0.5 138.0 112.3 127.17 145.23 81.58 158.37 349.01 135.91
0.6 132.3 115 129.92 153.20 81.89 162.58 351.19 137.95
0.7 120.4 117.4 132.25 159.95 82.14 166.14 353.08 139.67
0.8 138.3 119.4 134.27 165.79 82.37 169.23 354.75 141.16
0.9 126.2 121.2 136.05 170.95 82.56 171.95 356.24 142.47
1.0 148.0 122.7 137.64 175.56 82.74 174.39 357.6 143.65
1.1 131.0 124.2 139.08 179.73 82.9 176.59 358.84 144.71
1.2 133.8 125.5 140.39 183.53 83.04 178.6 359.99 145.68
1.3 132.3 126.7 141.6 187.04 83.18 180.45 361.05 146.58
1.4 126.9 127.8 142.72 190.28 83.3 182.16 362.05 147.41
1.5 135.1 128.9 143.77 193.30 83.42 183.75 362.98 148.18

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Figure 2. Path loss (dB) versus distance (km) for Okumura-Hata, COST-231-Hata, Ericsson 999, Lee, SUI,
ECC-33, and modified model for FUTO University campus MTN network
Figure 2 was generated from the data presented in Table 2 The pathloss values at a distance of
0.10 km from the base station is 87.96, 102.85, 74.80, 78.89, 121.19, 332.16, and 117.95 dB. Additionally, the
route losses measurements at a 1.5 km distance were 128.9, 143.77, 193.297, 83.42, 183.75, 362.98, and
148.18 dB. As a result of the towering trees, structures, and topography, the ECC-33 model overestimates the
propagation path loss numbers, whereas Lee model did really poorly. The results demonstrate that the current
path loss models under consideration are not appropriate for predicting propagation in the environment. The
results from the modified model are most in line with the measurements. As a result, the modified model is
appropriate for network planning and can be used to calculate the path loss coverage in Nigeria’s FUTO
University campus.
4.1. Validation of the propagation model
This section provides a description of the performance analysis based on the model’s accuracy by
utilizing statistical analysis methods such as: RMSE and MAPE. The amount of variation and error in the
measured data determines how effective the proposed model is. In terms of the error rate between measured
and anticipated values, these performance metrics will compare and validate the original and changed models.
The MAPE and RMSE were calculated between the output of the existing models and the measured path loss
data. As shown in (35), and (36) contain the following expressions by [1], [12].
RMSE (dB) = √
1
n
∑ (ppm − ppr)2
n
i=1 (35)
MAPE (dB) =
1
n
∑ |
ppm−ppr
ppm
|
n
i=1 x100 (36)
where 𝑝𝑝𝑚 is the mean of measured data, 𝑝𝑝𝑟 is the mean value of predicted path loss, and 𝑛 is the number of
data points.
Apart from ECC-33 model which overestimates the propagation path loss numbers, according to the
performance evaluation, the Ericsson model, SUI model and Lee model have a comparably large RMSE and
MAPE values. Modified model and COST-231 are the models with closest value to the measured value, with
RMSE and MAPE values of 8.31; 0.453 dB and 9.73; 0.498 dB respectively. Table 3 contains information on
the performance evaluation.
Table 3. Shows the results of error computation from existing and modified models
Okumura-Hata (dB) COST-231 (dB) Ericsson (dB) Lee (dB) SUI (dB) ECC-33 (dB) Modified (dB)
RMSE 14.33 9.73 25.79 48.4 33.76 222.1 8.31
MAPE 0.733 0.498 1.317 2.474 1.725 11.35 0.453
5. CONCLUSION
The path loss has been evaluated in the study using six existing models: The Okumura-Hata,
COST-231, Ericsson 999, Lee, SUI, and ECC-33 models. Although there are various propagation models to
forecast route loss, the findings of this paper support the idea that they are not accurate in predicting the

 ISSN: 2088-8708
6498
coverage of the network in locations other than those for which it was intended. This study used a 2.3 GHz
MTN base station to conduct measurements at various sites on the FUTO University campus in Owerri. An
estimated value of the path loss was determined using the measured received signal strength (MRSS) of data
gathered at various distances from mobile station to base station. By modification of the log distance shadowing
model, the derivative of the path loss exponent obtained as 𝑣 = 2.57 and the standard deviation σ=10.75 dB
about a mean value were substituted in the log distance shadowing model to establish the modified path loss
model for FUTO University environment. The addition of Standard deviation σ in the modified path loss model
compensated for the error in the measured pathloss. Based on the amount of error on the exiting and improved
model, utilizing RMSE and MAPE, the performance analysis and validation of the models were estimated. The
improved model’s performance estimation yielded the best result, demonstrating its applicability for path
attenuation prediction in network coverage in the study area. It is recommended that this research is extended
to other study area and machine learning prediction methods is adopted in future study.
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BIOGRAPHIES OF AUTHORS
Akande Akinyinka Olukunle received his B. Tech (2008) in Electronic and
Electrical Engineering from Ladoke Akintola University of Technology (LAUTECH),
Ogbomoso. M. Eng in Electrical and Electronic Engineering and a Ph.D. in Communication
Engineering from University of Ilorin and LAUTECH in 2013 and 2019, respectively. He is
presently a Lecturer at Federal University of Technology Owerri. His research area is in
5G/6G wireless communication technologies, resource management in cognitive radio
networks and intelligence cyber-physical systems. He can be contacted at email:
olukunle.akande@futo.edu.ng.
Akinde Olusola Kunle holds a Bachelor of Engineering (B. Eng) in Electrical and
Electronic Engineering from the University of Port Harcourt, Nigeria, Master of Engineering
(M. Eng) and Ph.D. degrees with specialty in Computer and Electronic Engineering from the
Federal University of Technology, Owerri, Nigeria. He is a Registered Engineer with the
Council for Registration of Engineering in Nigeria (COREN); a member of the Nigerian Society
of Engineers (NSE), As well as, a Member of the Institute of Electrical and Electronic
Engineering (IEEE), United State of America (USA). He is currently a Senior Lecturer in the
Department of Electrical and Electronic Engineering, the First Technical University, Ibadan,
Nigeria. Areas of his research interests include real-time embedded and distributed systems,
intelligent systems, data communication, security and monitoring system. She can be contacted
at email: olusola.akinde@tech-u.edu.ng.
Odeyinka Oluwadara Joel obtained his B. Tech in Electronic and Electrical
Engineering from Ladoke Akintola University of Technology, Nigeria and M. Eng in Electronic
and Electrical Engineering (Communication Engineering Option) from Federal University of
Technology, Owerri, Nigeria. He is currently a lecturer at First Technical University, Ibadan.
His research interest is energy optimization in wireless sensor networks (WSNs) using machine
learning approach, IoT based WSNs for telemedicine, cognitive radio, and microwave
communication. He can be contacted at email: oluwadara.odeyinka@tech-u.edu.ng.

 ISSN: 2088-8708
6500
Ilori Abolaji Okikiade is currently a Ph.D. student of Communication and Digital
Signal (Electrical and Electronic Engineering) at Federal University of Agriculture, Abeokuta,
Ogun State, Nigeria, he holds a Master’s degree in Electrical and Electronic Engineering
(Communication) from University of Lagos, Nigeria in 2014 and a B. Tech in Electrical and
Electronic Engineering (Communication) from Ladoke Akintola University of Technology,
Ogbomoso. Oyo State, Nigeria in 2008). He lectures at the First Technical University, Ibadan,
Nigeria in the Department of Electrical and Biomedical Engineering and his research interests
are in wireless communication, radio waves propagation, system engineering and renewable
energy. He can be contacted at email: abolaji.ilori@tech-u.edu.ng.
Adigun Matthew Olusegun retired in 2020 as a Senior Professor of Computer
Science at the University of Zululand. He obtained his doctorate degree in 1989 from Obafemi
Awolowo University, Nigeria; having previously received both Masters in Computer Science
(1984) and a Combined Honors degree in Computer Science and Economics (1979) from the
same University (when it was known as University of Ife, Nigeria). A very active researcher in
software engineering of the wireless internet, he has published widely in the specialized areas
of reusability, software product lines, and the engineering of on-demand grid computing-based
applications in Mobile Computing, Mobile Internet, and ad hoc Mobile Clouds. Recently, his
interest in the wireless internet has extended to wireless. He has received both research and
teaching recognitions for raising the flag of Excellence in Historically Disadvantaged South
African Universities as well as being awarded a 2020 SAICSIT Pioneer of the year in the
Computing Discipline. Currently, he works as a Temporary Senior Professor at the Department
of Information Technology, Cape Peninsula University of Technology to pursue his recent
interest in AI-enabled pandemic response and preparedness. He can be contacted at email:
profmatthewo@gmail.com.
Ajagbe Sunday Adeola is a Ph.D. candidate at the Department of Computer
Engineering, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria and
a Lecturer, a First Technical University, Ibadan, Nigeria. He obtained M.Sc. Computer
Engineering at LAUTECH, M.Sc. and B.Sc. in Information Technology and Communication
Technology respectively at the National Open University of Nigeria (NOUN). His specialization
includes artificial intelligence (AI), natural language processing (NLP), information security,
communication, and internet of things (IoT). He has many publications to his credit in reputable
academic. He can be contacted at email: sunday.ajagbe@tech-u.edu.ng.

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