https://doi.org/10.37815/rte.v34n3.947

Original articles

Comparison of radio propagation models in five LTE coverage cells in Riobamba

Comparación
de modelos de propagación de radio en cinco celdas de cobertura LTE de Riobamba

Brayan Vique1 https://orcid.org/0000-0001-6283-0884,

Carlos Bayas1  https://orcid.org/0000-0002-3037-3954, Martin Escobar1 https://orcid.org/0000-0001-7033-2914, Adrián Infante1 https://orcid.org/0000-0001-8754-9239, Allison Proaño1 https://orcid.org/0000-0002-8587-2099

1Escuela Superior Politécnica de Chimborazo, Facultad
de Informática y Electrónica, Riobamba, Ecuador

[email-masked], [email-masked], [email-masked], [email-masked], [email-masked]

Sent:              2022/06/19

Accepted:       2022/09/16

Published:       2022/11/30

Abstract

This article performs a comparative analysis of the power intensity
levels measured with the Network Cell Info Lite application and the performance
of the different propagation models: Log-Normal, Okumura Hata,
COST 231, Walfish Bertoni,
and SUI in the 4G LTE Frequency Band. The study was conducted in five LTE
coverage cells located in the southern area of the city of Riobamba. The model
that best fits each cell was chosen by means of absolute error analysis, thus
obtaining an empirical correction factor for the proposed models. For the
analysis of the absolute error, three measurement campaigns were carried out
with 50 samples where the mean value was obtained. After applying the aforementioned models, the Log-Normal model yielded the most
favorable results, being the one that achieved the best adaptation in Riobamba,
since the power levels vary in the range (-80; -106) dBm at a coverage area not
exceeding 200m.

Keywords: Coverage cells, frequency band, multiscreen diffraction loss, cells and open spatial propagation model, Friis.

Summary: Introduction,
Theoretical Framework, Methodology, Results and Conclusions.

How to
cite: Vique, B., Bayas,
C., Escobar, M., Infante, A. & Proaño, A. (2022). Comparación de
modelos de propagación de radio en cinco celdas de cobertura LTE de Riobamba.
Revista Tecnológica - Espol, 34(3), 171-190. http://www.rte.espol.edu.ec/index.php/tecnologica/article/view/947

Resumen

Este artículo realiza un análisis
comparativo de los niveles de intensidad de potencia medidos con la aplicación
Network Cell Info Lite y el desempeño de los diferentes modelos de propagación:
Log-Normal, Okumura Hata, COST 231, Walfish Bertoni y SUI, en la Banda de
Frecuencia 4G LTE. El estudio se realizó en cinco celdas de cobertura LTE
ubicadas en la zona sur de la ciudad de Riobamba. Se eligió el modelo que mejor
se ajusta a cada celda mediante el análisis de error absoluto, con ello se
obtuvo un factor empírico de corrección para los modelos propuestos. Para el
análisis del error absoluto se realizaron tres campañas de medición con 50
muestras donde se consiguió el valor medio. Después de aplicar los modelos
antes mencionados, el modelo Log-Normal arrojó los resultados más favorables
siendo este, el que logró una mejor adaptación en Riobamba ya que los niveles
de potencia varían en el rango (-80; -106) dBm a una zona de cobertura no
superior a los 200m.

Palabras
clave: Celdas de cobertura, banda de frecuencia, pérdida
de difracción multipantalla, celdas y modelo de
propagación espacial abierta, Friis.

Introduction

The growing demands on mobile services have encouraged many researchers
toward achieving multi- services with low latency. To illustrate that, Zhang
(2012)  pointed out that LTE (Long Term Evolution) is
a standard for high-speed wireless data communications which is maintained as a
project of the 3rd Generation Partnership Project (3GPP). In addition, to cover
the requirements of the mobile migrations of Internet applications, such as
VOIP, video streaming, music downloading, and mobile TV, LTE networks offer the
capacity to tolerate the throughput explosion for the connection from mobile
devices customized to those new mobile applications. A propagation model is a
set of mathematical expressions, diagrams, and algorithms used to represent the
radio characteristics of a given environment (Bekele, 2017).

In 2004, an initial study of long-term evolution (LTE) was introduced
and viewed as a path for migration to 4G networks (Rao, 2009). With the rapid
development of LTE (4G) technology in recent years, 4G terminals like mobile
phones have been widely used for communication. LTE (4G) signals have covered
indoor and outdoor environments in modern cities. Inspired by the advances in
wireless sensing that have enabled a large variety of new applications such as
indoor localization (Li, 2016).

LTE aims to increase the speed and capacity of wireless networks by
utilizing signal processing techniques and modulations (Hadi,
2015). 4G technology is supported by the 3GPP (third generation) standard,
which bases its system on IP, that is, it is a system of systems and a network
of networks, and is subsequently overcome in the convergence between cable
networks or wireless networks, computers, electrical devices -electronic, ICT
among others to provide access speeds between 100Mbps in movement and 1Gbps at
rest, but the most important thing is to maintain the quality of service (QoS)
from point to point (end-to-end), with high security  to massify the number of additional services
in any place betting on having the lowest possible cost (Tomažič,
2009).

Path loss models are sophisticated tools for predicting coverage area,
interference analysis, frequency assignments, and cell parameters which are the
basic elements for the network planning process in mobile radio systems (Blaunstein, 2006).

The Okumura-Hata model is the most widely used
empirical propagation prediction model. In 1980, Hata
introduced an empirical formula for propagation loss that was derived from
Okumura's report to put the propagation prediction method into computational
use in system planning software. The propagation loss is presented in the
simple form A + B log(R), where A and B are functions of frequency and height
of the antenna and R is the distance (Y., 1967). This simplicity of the model
has made it the most widely used propagation prediction model and it is even
standardized for international use (Union, 1995).

The European Cooperative for Scientific and Technical Research
(Euro-Cost) developed the Cost 231 model, in which the Hata
model is extended to the 2 GHz range, covering the VHF and UHF bands. CM is a
correction factor to fit the model by extending the frequency range for which
the Hata model operates; CM (0 dB) for medium cities
and suburban areas; CM (3 dB) for metropolitan centers; and that corresponds to
the equations presented in the Hata model. One of the
contributions of this model is to consider losses due to dispersion (Garcia,
2004).

Stanford University Interim (SUI) model derived from Hata,
with corrections for frequencies above 1900MHz. It includes the path loss
exponent, it proposes three different types of terrain: urban, suburban, and
rural. The height of the antenna of the proposed base station is between 10 and
80 meters, that of the mobile from 2 to 10 meters, and the extension of the
cell from 0.1 to 8 km (Shahajahan, 2009).

The model, proposed by Joram Walfisch and Henri Bertoni,
considers the losses produced by the diffractions that occur on the roofs of
buildings (Walfish, 1988). It is a model that does
not consider the existence of a line of sight between the transmitter and the
receiver, it uses the phenomenon of diffraction to describe the losses suffered
by the signal before reaching the receiver located low above the street. The
contribution of the rays that penetrate the buildings and of those that suffer
multiple diffractions is neglected. The separation between the buildings must
be less than their height and they are supposed to be arranged in parallel
rows. The frequency range in which this model applies is from 300 to 3,000 MHz,
with a separation between transmitter and receiver of 200 to 5,000 m. It is not
applicable when the base station antenna is below the average height of
buildings (Hernando, 2013).

The log-normal distribution is a function distributing a dependent
variable in a normal or Gaussian fashion on a logarithmic scale of the
independent variable. This function has been used for a long time to describe
size distributions of particle properties in atmospheric aerosols. Foitzik (1964) used this functional relationship for the
description of optical aerosol properties. Later, Whitby (1974) built a general
concept for the multimodal nature of the atmospheric aerosol on this approach
by fitting measured particle size distributions in a combination of three
log-normal distributions (Heintzenberg, 1994).

To
determine the performance of the previously proposed propagation models, a
comparative analysis of the power intensity levels measured with the Network
Cell Info Lite application in the 4G Frequency Band was carried out at 5 LTE
coverage cells located in the southern zone of the Riobamba city and the model
that best adapts to the conditions of the area will be extinguished by applying
an empirical correction factor to the proposed models. The document details the
absolute error for the calculation of this correction factor, considering three
measurement campaigns with 50 samples where the mean value was obtained. From
the study carried out, it was concluded that the model Log-Normal is the best
estimator of power levels considering the environment of the city, which is a
residential area.

Theoretical Framework

Log-Normal Model

It is an empirical model based on a reference of
the losses at a pre-established distance, and applicable in closed environments
by factors of correction. It is expressed in an equation as a function of the
distance between transmitter and receiver as:

(1)

Where is the path loss variable due to the multiple
trajectories;

is
the- loss to one near reference distance

and
it is calculated using the space propagation model open, Friis formula, or
taking measurements field;

is a
random variable expressed in dB and experimentally validated and statistically.
The model is simple but absorbs the random effects of shadows
and multipath that occur for different measurement locations with the same
distance between the Tx-Rx, but with different obstructions in the path of
propagation comparing with a reference value (Yepez, 2012).

Okumura Hata
Model

The Hata
model is an empirical formulation of the graphical path loss data provided by
Okumura and is valid from 150 MHz to 1500 MHz. Hata presented the urban area propagation loss as a
standard formula and supplied correction equations for application to other
situations. The standard formula for median path loss in an urban area is
given:

(2)

Where

is the frequency (MHz) from 150 MHz to 1500
MHz,

is the effective
transmitter (base station) antenna height (meters) ranging from 30 m to 200 m,

is the effective receiver (mobile) antenna
height (meters) ranging from 1 m to 10 m,

is the T-R separation distance (km), and

is the correction factor for effective mobile
antenna height which is a function of the size of the coverage area. For a
small to medium-sized city, the mobile antenna correction factor is given by:

(3)

and for a large city, it is given by:

for

MHz

(4)

for

MHz

(5)

The predictions of the
Hata model compare very closely with the original
Okumura model if d exceeds 1 km. This model is well suited for large cell
mobile systems, but not personal communications systems (PCS) which have cells
on the order of a 1km radius (Anonymous, 1968).

Cost 231 Walfish-Ikegami Model

The COST 231 model is a semi-empirical path loss
prediction model. It is recommended for macro-cells in urban and sub-urban
scenarios, with good path loss results for transmitting antennas located above
average rooftop height. However, the error in the predictions increases
considerably as the transmitter height approaches rooftop height, leading to
very poor performance for transmitters below that level. Compared to previous
models such as Okumura-Hata, the COST 231 model
includes a series of additional parameters to the calculation process, in
addition to expanding the frequency range in which it can be used (800 - 2000
MHz). The model performs a more detailed calculation of the attenuation, based
on four additional parameters:

·
Height of buildings.

·
Width of streets.

·
Separation between buildings.

·
Orientation of the street concerning the
direction of propagation.

For LOS scenarios, the propagation loss considers
only the free space loss,

where:

(6)

is
expressed in km and

in
MHz.

The typical NLOS path described in the COST 231
model is shown in Figure 1
and Figure 2.

Figure 1

Typical NLOS Propagation Scenario Profile view

Figure 2

Typical NLOS Propagation Scenario Top view

The parameters defined in the COST 231 model (Figure 2) are the
following:

·

:
average height of buildings (meters)

·

:
width of the street (meters)

·

:
average distance between buildings (meters)

·

:
angle formed by the direction of propagation and the axis of the street.

·

:
base station antenna height (meters)

·

:
height of the mobile device antenna (meters)

·

=

(meters)

·

=

(meters)

·

:
total distance between the first and the last building on the path (meters)

·

:
distance between the base station and mobile device (km)

·

:
frequency (MHz)

The basic propagation loss for the NLOS scenario is
given by:

(7)

The propagation loss in free space conditions, L0,
is obtained according to the expression:

(8)

The term

considers the width of the street and its orientation
concerning the direction of ray propagation.

The expression for the calculation of

is
given by:

(9)

where:

(10)

The

term
is a correction factor that quantifies losses due to street orientation. In
case the value of

<
0,

= 0
should be considered.

The multiscreen diffraction loss,

, is a function of the frequency, the
distance between the mobile device and the base station, as well as the height
of the base station and the height of buildings. Like

, if

is
negative,

= 0
is considered. Its value is calculated using the expression:

(11)

where:

(12)

Is a term that depends on the height of the base
station. In addition, the following parameters are defined:

(13)

(14)

(15)

The

term
presents the increase in path loss for the case of base stations located below
the average height of the buildings. The terms

and

control the dependence of

on
distance and frequency, respectively. If there is no data on the buildings on the route, the COST 231 model recommends using:
(Aguilar, 2010).

(16)

(17)

Walfish-Bertoni Model

It is valid when there is no line of sight between
the base station and the mobile. Buildings are modeled as a set of diffraction
and absorption screens buildings of a uniform height and width are considered,
require that the transmitting antenna is above the highest ceiling.

The path loss is given by:

(18)

where:

·

: frequency (MHz)

·

: distance between transmitter and receiver
(km)

·

: average height of the antenna concerning
the height of the buildings (m)

·

: variable that expresses the influence of
buildings on the signal

Figure 3

Typical
propagation Scenario

The influence of buildings on the signal (Figure 3).

(19)

where:

·

: height of buildings (m)

·

: receiver height (m)

·

: distance between buildings (m)

In a real environment, the geometry of the
buildings is irregular, causing this model to have less certainty in predicting
the received power; however, this model is applicable in radio propagation
simulation software (Cell View) if adaptations are made to the equations or if
an average density and height of buildings are obtained (Shabbir, 2011).

SUI Model

Stanford University
Interim (SUI) model is developed for IEEE 802.16 by Stanford University. It is
used for frequencies above 1900 MH. In this propagation model, three different
types of terrains or areas are considered. These are called terrain A, B, and
C. Terrain A represents an area with the highest path loss, it can be a very
densely populated region while terrain B represents an area with moderate path
loss, a suburban environment. Terrain C has the least path loss which describes
a rural or flat area. In Table 1, these different terrains and different factors
used in the SUI model are described (Table 1).

Table 1

Different terrains and their parameters

PARAMETERS

TERRAIN A

TERRAIN B

TERRAIN C

a

4.6

4

3.6

b (1/m)

0.0075

0.0065

0.005

c (m)

12.6

17.1

20

The path loss
in the SUI model can be described as

(20)

where PL represents Path Loss in DBS,

is
the distance between the transmitter and receiver, do is the reference
distance,

is
the frequency correction factor,

is
the correction factor for base station height, S is shadowing and

is
the path loss component and it is described as

(21)

where

is
the height of the base station and a, b and c represent the terrain for which
the values are selected from the above table.

(22)

where

is
free space path loss while do is the distance between Tx and Rx and

is
the wavelength. The correction factor for frequency and base station height are
as follows:

(23)

(24)

where

is
the frequency in MHz, and

is
the height of the receiver antenna. This expression is used for terrain types A
and B. For terrain C, the blow expression is used.

(25)

(26)

Here,

dB for rural and suburban environments
(Terrain A & B) and 6.6 dB for urban environments (Terrain C) (Shabbir,
2011).

Methodology

The
comparative analysis was carried out on 5 LTE coverage cells in the South Zone
of the city of Riobamba in 3 different campaigns, taking 50 reference samples
from which, the arithmetic means of said measurements were taken to have an
estimate of the reception power of each cell.

Figure 4

Location of LTE
coverage cells

The southern area of ​​the city
of Riobamba was taken by the Google Maps application where the 5 cells were
located, and the power data was collected for the study of the propagation
models that can be applied in said area. Each antenna works with different
operators (Figure 4).

The applicability of this document was
carried out using 2 software programs: Microsoft Excel for the development of
mathematical operations and obtaining numerical data, and MATLAB for the
management of graphs and the statistical study of results.

To determine which model best fits, we apply
the equations of the different propagation models considering the correction
factor and the parameters that each model requires.

Coverage Cell 1

The first cell is located on José Orozco and
Bernardo Darquéa streets from Riobamba City it
belongs to the Tuenti operator and works with an
operating frequency of 1900MHz. Next, the considerations for the use of
propagation models are detailed (Table 2).

Table 2

Parameters Cell 1

PARAMETERS

VALUE

Base station antenna height (m)

30

Mobile device height (m)

1.5

Distance (m)

47–170

Street width (m)

4

Elevation angle

14.47°-40.79°

Buildings height (m)

9

Distance between buildings (m)

4

Fuente: https://www2.ulpgc.es/hege/almacen/download/27/27199/propagacion.pdf

Coverage Cell 2

The second cell is in the Sabún Sports Complex, located on Av. October 9 and Atenas street from the Riobamba city, it belongs to Tuenti operator and works with an operating frequency of
1900MHz. Next, the considerations for the use of propagation models are
detailed (Table 3).

Table 3

Parameters Cell 2

PARAMETERS

VALUE

Base
station antenna height (m)

32

Mobile device height
(m)

1.5

Distance (m)

48.62 – 205.02

Street width (m)

4

Elevation angle

20° - 63°

Buildings height (m)

9

Distance between buildings (m)

3.5

Coverage Cell 3

The third cell is located at Celso Rodríguez
Avenue and París street from
Riobamba city, it belongs to the Tuenti operator and
works with an operating frequency of 1900MHz. Next, the considerations for the
use of propagation models are detailed (Table 4).

Table 4

Parameters Cell 3

PARAMETERS

VALUE

Base station antenna height (m)

18

Mobile device height (m)

1.5

Distance (m)

49 – 211

Street width (m)

4

Elevation angle

25° - 63°

Buildings height (m)

9

Distance between buildings (m)

4.5

Coverage Cell 4

The fourth cell is located at Leopoldo
Freire Avenue and Lisboa Street from Riobamba city,
it belongs to the CNT operator and works with an operating frequency of
1900MHz. Next, the considerations for the use of propagation models are
detailed (Table 5).

Table 5

Parameters Cell 4

PARAMETERS

VALUE

Base
station antenna height (m)

30

Mobile device height
(m)

1.5

Distance (m)

49 – 226

Street width (m)

4

Elevation angle

25°-72°

Buildings height (m)

9

Distance between buildings (m)

3.5

Coverage Cell 5

The fifth cell is located at 9 de Octubre avenue and Noruega street
from Riobamba city, it belongs to the Claro operator and works with an operating
frequency of 1700MHz. Next, the considerations for the use of propagation
models are detailed (Table 6).

Table 6

Parameters Cell 5

PARAMETERS

VALUE

Base station antenna height (m)

25

Mobile device height (m)

1.5

Distance (m)

48.62 – 205.02

Street width (m)

4

Elevation angle

17° - 70°

Buildings height (m)

9

Distance between buildings (m)

4

Finally, the losses obtained by each of the
models mentioned above are replaced in the general formula that is given by:

(27)

where:

·

: transmission
potency

·

: transmission gain

·

: reception gain

·

: propagation models lost

The received power obtained was plotted
against the distance around each of the base stations for each propagation
model and compared to the measured field average power value.

Adjusting the
Propagation Model

Since the propagation
models were taken in cities whose conditions are very different from the study
area, it is necessary to adjust the model. For this, the absolute error that
will represent the empirical correction factor was used to determine with what
range of power the values ​​are far from the real ones measured. To
do this, the average of the measured values ​​and the average of
each of the propagation models that required adjustment is used.

(28)

where:

·

: absolut
mistake

·

:  average power value

·

: power value for each
propagation model

Results

The
following graphs obtained show the approximate curves of the propagation models
without a correction factor in each of the five LTE coverage cells concerning
the average power whose data was collected in the southern zone of the city of
Riobamba.

Figure 5

Coverage cell 1
without correction factor

Figure 5In cell 1
(Figure 5). It is possible to observe that without the
corrections to the different propagation models, the samples obtained resemble
the Log Normal model, in addition to the other models such as Okumura-Hata and the Walfish-Bertoni
model, giving results very far from the values measured.

Figure 6

Coverage cell 2
without correction factor

In cell 2 (Figure 6) it is possible to observe that the samples
obtained are similar to the Log Normal model within 100 meters, which is where
there is a great concentration of powers, so it can be said that it is where it
is best has been coupled to the prediction model, considering that the
different attenuations are due to being in an area full of vegetation. On the
other hand, the other models do not coincide with the powers collected because
the area where the samples were taken does not meet the characteristics of the
different models, which is why it is seen that both the Okumura-Hata, Cost 231 and Walfish-Bertoni
are far apart with respect to the powers obtained and compared with the data
obtained in the other radio bases, it is observed that these 3 models are much
further away from each other.

Figure 7

Coverage cell 3
without correction factor

In cell 3 (Figure 7) it is observed
that the data collected is very similar to the Log Normal model, especially
between 80 and 118 meters there is coupling with respect to this model, but at
the same time, it is possible to observe that the powers collected at from 130
to 190 meters they resemble the SUI model, these power losses are because the
area where the data was collected was an area where there are many houses
between 2 and 3 floors.

Figure 8

Coverage cell 4 without correction factor

In cell 4 (Figure 8) where it is
observed that it resembles two models, firstly, the collected data tend to have
a coupling with the SUI model between 47 to 78 meters, and from 83 to 140
meters they are coupled to the model. Log Normal, considering that from 140
meters these tend to suffer more attenuations, therefore they move away from
the results of the models, this is because the data obtained were in a place
full of buildings within the main street.

Figure 9

Coverage cell 5 without empirical correction
factor

In cell 5 (Figure 9) it is observed
that it resembles the Log Normal model within 50 to 96 meters of distance,
where there is a great concentration of powers between 70 to 94 meters, in
addition to that manages to observe that there is an intersection between the
SUI model and Log Normal, so that within 130 to 135 meters the data collected
resembles the SUI model.

Next, the graphs of the propagation models
are presented with a correction factor so that they are coupled to the power
measurements of the 5 different LTE coverage cells located in the southern zone
of the city of Riobamba, in this way having a prediction of attenuation in that
area.

Figure 10

Coverage cell 1 with correction factor

In cell 1 shown in (Figure 10) the correction
factor was applied to 3 propagation models: Okumura-Hata,
Cos 231, and the Walfish-Bertoni model, the
correction values ​​were: -20.69 dBm, -13.34 dBm, -21.23 dBm
respectively. When applying the correction factor, the graphs of the
propagation models are better coupled to the power samples taken with the
mobile, however, the model that is more coupled in cell 1 is the Log-normal
model.

In the first 100 meters, many powers were
collected, finally, it was visualized that at a greater distance the samples
suffer an attenuation that is caused by the presence of different
infrastructures. It is possible to observe that, without the corrections to the
different propagation models, the samples obtained are similar
to the Log Normal model, in addition to other models such as the
Okumura-Hata model and the Walfish-Bertoni
model, giving results very far from those obtained. measured values. According
to Pérez, within his investigation he affirms the following:

Attenuation
as a result of energy absorption by the medium occurs
as a consequence of the electromagnetic characteristics of the material through
which the wave propagates and is the fundamental cause of energy losses in the
case of material media. (p. 111). Pérez (2001)

That is why within the first 100 meters
different powers were collected, finally it was visualized that at a greater
distance the samples suffer an attenuation that is caused by the presence of
different infrastructures.

Figure 11

Coverage cell 2 with correction factor

In cell 2 shown in (Figure 11) the correction
factor was applied to 3 propagation models: Okumura-Hata,
Cos 231, and the Walfish-Bertoni model, the
correction values ​​were: -30.35 dBm, -19.40 dBm, - 26.20 dBm
respectively. When applying the correction factor, the graphs of the
propagation models performed better than the power samples taken with the
mobile, however, the model that best fits cell 2 is the Log-Normal with
variations less than 5dBm.

In cell 2 (Figure 6) it can
be seen that the samples obtained are similar to the Log Normal model
within 100 meters, which is where there is a large concentration of powers, so
it can be said that it is where there is a better coupling to the prediction
model, considering that the different attenuations are due to being in an area
full of vegetation. According to Recommendation ITU-R P.833-3, it states:

In
certain cases, vegetation attenuation may be important, both for terrestrial
systems and for Earth-to-space systems. But the great diversity of conditions
and types of foliage makes it difficult to develop a general prediction
procedure. In addition, there is a lack of adequately verified experimental
data. (p.1). Recommendation UIT-R P.833-3 (2001)

On the other hand, the other models do not
coincide with the powers collected because the area where the samples were
taken does not meet the characteristics of the different models, so it is seen
that both the Okumura-Hata, Cost 231, and Walfish- Bertoni are very far
apart concerning the powers obtained and comparing with the data obtained in
the other radio bases, it is observed that these 3 models are much further away
from each other.

In cell 3 shown in (Figure 12) correction factor
was applied to 4 propagation models: Okumura-Hata,
Cos 231, the Walfish-Bertoni model, and the SUI
model, the correction values ​​were -34.20 dBm, - 27.28 dBm, -34.42
dBm, and -6.69 dBm respectively. When applying the correction factor, the
graphs of the propagation models performed better than the power samples taken
by the mobile, however, we see that almost all the models fit correctly, being
the one that stands out the most are the Cos 231 model and the Cos 231 model.
Log-Normal with differentiation between the measured value and that of the
power of the models not greater than 6 dBm.

Figure 12

Coverage cell 3 with correction factor

In cell 3 (Figure 7) it is observed
that the collected data are very similar to the Log Normal model, especially
between 80 and 118 meters there is coupling concerning this model, but at the
same time it is possible to observe that the powers captured from 130 to 190
meters are similar to the SUI model, these power losses are because the area
where the data was collected is an area where there are many houses between 2
and 3 floors. For this reason, Anonymous (2019), suggests that "whenever
there is a refraction of the electromagnetic wave, we will have a reflection of
energy on the separation surface, for which the energy of the refracted wave
will be less than that of the incident" (p.2). Anonymous (2019).

Figure 13

Coverage cell 4 with correction factor

In cell 4 shown in (Figure 13) the correction factor was applied to 3
propagation models: Okumura-Hata, Cos 231, the Walfish-Bertoni model, the correction values
​​were: -31.84 dBm, -14.43 dBm, -20.64 dBm, respectively. When
applying the repair factor, the graphs of the propagation models are better
coupled to the power samples taken by the mobile, however, the curve of the
Okumura-Hata model, together with the normal Log
model, was the most adapted, even though between 140m and 200m there is a
significant dispersion in the measurements whose differentiation is less than 8
dBm.

In cell 4 (Figure 8) where it is observed that it resembles two
models, first of all, the collected data tend to have a coupling with the SUI
model between 47 to 78 meters, and from 83 to 140 meters are coupled with the
Log Normal model, considering that from 140 meters these tend to suffer more
attenuation, according to Recommendation ITU-R P.1407-1, states:

In radio systems with low antenna heights,
there are often multiple indirect paths between the transmitter and receiver
due to reflection from surrounding objects, in addition to the direct path when
there is a line of sight. This multipath propagation is particularly important
in urban environments where building walls and paved surfaces generate strong
reflections. (p.1). Recommendation UIT-R P.1407-1 (2003)

Therefore, they are
far from the results of the models, this is because the data obtained was in a
place full of buildings within the main street.

Figure 14

Coverage cell 5 with
correction factor

In cell 5 shown in (Figure 14) correction factor
was applied to 3 propagation models: Okumura-Hata,
Cos 231, and the Walfish-Bertoni model, the
correction values ​​were: -30.29 dBm, -21.97 dBm, -29.48 dBm, respectively.
When applying the correction factor, the graphs of the propagation models are
better coupled to the power samples taken by the mobile, however, the one that
best fits is the normal Log model curve with a differentiation between the
measurements taken and the model less than 5dBm.

One of the technologies and methods used is
the repeater or signal amplifier. In this case, the signal received from a
cellular operator is amplified to provide coverage inside a building. The
building or area to be covered can be any building, house, garage, or factory
where the mobile phone signal is weak, and we want to amplify it. (p.n). Ubierna (2018)

In addition to the
fact that it is possible to observe that there is an intersection between the
SUI model and Log Normal, this is because these models do not consider losses
due to reflection from different infrastructures, and within 130 to 135 meters
the data collected resembles the SUI model.

Conclusions

·
The use of these models and the application of the correction factor
allowed us to analyze the values of reception power and real propagation losses
in particular environments of the southern zone of Riobamba city.

·
The samples obtained show a significant improvement with the use of the
Correction Factor, thus increasing the accuracy of predicting power levels.

·
In the southern part of Riobamba city, as it is a residential area
without the existence of large buildings, the signal loss does not exceed -106
dBm. Despite also the existence of natural topographic factors that influence
it.

·
According to the results obtained, it was concluded that the Log-Normal
model is the best predictor of power in the southern area of the city of
Riobamba considering the different scenarios where there were propagation
losses.

·
In the case of using these prediction models in practice, the data must
be modeled by software to determine the coverage and power of the signal that
the physical equipment must emit.

References

Anonymous.
(1968). Okumura model. Mobile Radio Propagation: Large-Scale Path Loss. https://elearning.espoch.edu.ec/pluginfile.php/1518133/mod_resource/content/1/sysc5608-AntProp-A21-A25.pdf

Aguilar, L.
(2010). Walfisch bertoni
model. Share and Discover Knowledge on SlideShare. https://pt.slideshare.net/eliche04/modelo-de-walfisch-bertoni

A.
(2019). Broadcast and TV reception. Spread, from
https://www2.ulpgc.es/hege/almacen/download/27/27199/propagacion.pdf

Bekele, N.,
& Demissie, B. (2017). Analyzing and modeling
of geo spatial effect on radio wave propagation system using geospatial
technologies. SCIRP Open Access. https://www.scirp.org/journal/paperinformation.aspx?paperid=81536

Blaunstein,
N., Toeltsch, M., Laurila, J., Bonek, E., Katz, D., Vainikainen, P., Tsouri,
N., Kalliola, K. & Laitinen, H. (2006). Signal power distribution
in the azimuth, elevation and time delay domains in
urban environments for various elevations of base station antenna. https://publik.tuwien.ac.at/files/pub-et_11863.pdf

Garcia, A., Ortega, H., Navarro, A. & Rodriguez, A. (2004). Effect of terrain on
electromagnetic propagation in urban environments on the Andean region, using
the COST231-Walfisch-Ikegami model and GIS planning tools. IEEE Xplore. https://ieeexplore.ieee.org/document/1353626

Hadi, S. & Tiong, T. (2015). Adaptive modulation and coding for LTE
wireless communication. IOP Conf. Ser.: Mater. Sci. Eng. 78 012016. https://iopscience.iop.org/article/10.1088/1757-899X/78/1/012016/pdf

Heintzenberg, J. (1994). Properties of the log-normal
particle size distribution, Leipzig, Germany. https://www.researchgate.net/publication/233157378_Properties_of_the_Log-Normal_Particle_Size_Distribution

Hernando,
J., Riera, J., & Mendo, T. (2013). Radio transmission: Vol. 7.
Radio transmission (7a ed.). Ramon Areces University
Publishing House.

ITU-R P.833-3,
R. (2001). Attenuation due to vegetation. International Telecommunications
Union. Retrieved August 11, 2022, from https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.833-3-200102-S!!PDF-S.pdf

ITU-R P.1407-1,
R. (2003). Multipath propagation and parameterization of its characteristics.
Multipath propagation and parameterization of its characteristics. from
https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.1407-1-200304-S!!PDF-S.pdf

Li,
X., Li, S., Zhang, D., Xiong, J. & Wang, Y. (2016). Dynamic-MUSIC:
Accurate device-free indoor localization. Heidelberg,
Germany. https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4391&amp;context=sis_research

Pérez Gracia, M. (2001, October 26). underground radar.
Evaluation for applications in archeology and historical-artistic heritage.
Retrieved on August 11, 2022, from
https://www.tdx.cat/bitstream/handle/10803/6216/13capitulo04.pdf?sequence#:~:text=La%20atenuaci%C3%B3n%20como%20resultado%
20of ,the%20case%20of%20means%20materials.

Rao,
A., Weber, A., Gollamudi. S. & Soni, R. (2009). LTE and HSPA+:
Revolutionary and evolutionary solutions for global mobile broadband | Semantic
Scholar. Semantic Scholar | AI-Powered Research Tool. https://www.semanticscholar.org/paper/LTE-and-HSPA+:-Revolutionary-and-evolutionary-for-Rao-Weber/c489aee76072912ed14804d439719cb5ef0d0b23

Shabbir, N., Sadiq, M. T.,
Kashif, H. y Ullah, R.
(2011). Comparison of radio propagation models for long term evolution (LTE)
network. https://elearning.espoch.edu.ec/pluginfile.php/1518137/mod_resource/content/1/1110.1519.pdf

Shahajahan, M. & Abdulla Hes-Shafi,
A. Q. M. (2009) Analysis of propagation models for wimax
at 3.5 GHz. http://www.diva-portal.org/smash/get/diva2:830403/FULLTEXT01.pdf

Tomažič,
S. & Jakuš, G. (2009). Long term evolution: Towards 4th generation of
mobile telephony and beyond. IEEE Xplore. https://ieeexplore.ieee.org/document/5339397

Union, I. T.
(1995). Recommendation ITU-R p.529-2 prediction methods for the terrestrial
land mobile service in the vhf and UHF bands. https://www.itu.int/dms_pubrec/itu-r/rec/p/R-REC-P.529-2-199510-S!!PDF-E.pdf#:~:text=Rec.%20ITU-R%20P.529-2%201%20RECOMMENDATION%20ITU-R%20P.529-2%20PREDICTION,BANDS%20(Question%20ITU-R%20203/3)%20(1978-1990-1995)%20Rec.%20ITU-R%20P.529-2

Ubierna, O. (2018). Cellular repeaters and distributed
antenna systems DAS. Wireless technology blog., from
https://www.comunicacionesinalambricashoy.com/wireless/repetidores-moviles-y-sistemas-de-antenas-distribuidos-das/

Walfish, J. & Bertoni, H. L.
(1988). A theoretical model of UHF propagation in urban environments. https://www.researchgate.net/publication/3007907_A_Theoretical_Model_of_UHF_Propagation_in_Urban_Environments

Y., Okumura.
(1967). Field strength and its variability in VHF and UHF land-mobile radio
service.  16:825-873.

Yepez, L. & Gragirena, M. (2012). Corroboration of the
"indoor" propagation model log-normal shadowing path loss model and
characterization of buildings electrical/electronic and classrooms. http://www.usfx.bo/nueva/vicerrectorado/citas/TECNOLOGICAS_20/Ingenieria%20Electrica/78.pdf

Zhang,
L., Xia, L., Liu, Z., Jing, J. & Ma, Y. (2012). Evaluating the Optimized
Implementations of SNOW3G and ZUC on FPGA. IEEE Xplore. https://ieeexplore.ieee.org/document/6296005