E03702038045

The International Journal Of Engineering And Science (IJES)
|| Volume || 3 || Issue || 7 || Pages || 38-45 || 2014 ||
ISSN (e): 2319 – 1813 ISSN (p): 2319 – 1805
www.theijes.com The IJES Page 38
A comparative Study between Modelled and Received Mobile
Phone Signal Strengths in terms of Power Loss and Statistical
Analysis
T. A Danladi A. U. Lawan and Salisu Umar
Department Of Electrical Engineeringbayero University Kano And Department Of Electrical & Electronic
Engineering, Hussaini Adamu Federal Polytechnic Kazaure, Jigawa State. Nigeria
--------------------------------------------------ABSTRACT-------------------------------------------------------
This paper focuses on propagation study in indoor environment which is one of the most complicated
propagation because of the effect of building structures and materials used in the construction and compares the
real received signal strength/power and the theoretical modelled one all considering loses. An important
requirement for mobile radio system is the provision of reliable services to the increasing number of users across
outdoor to indoor interface. This research work studied, by way of extensive measurements. Five geographical
locations at different distances in steps were used as case study within Kaduna metropolis in Nigeria namely:
AnguwanKanawa, AnguwanMuazu, Hayen-Danmani, Mararaban Jos and Kabala. Signals radiated from four
GSM (namely, MTN, Etisalat, Glo, starcomm and Airtel) and one CDMA (Stacom) which are international
service providers were considered
KEY WORDS: GSM, CDMA, modelled power, loss.
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Date of Submission: 27 December 2013 Date of Publication: 20 July 2014
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I. INTRODUCTION
There are several factors that affect radio wave propagation which result in the degradation of signals,
these include multipath fading effect, non-line-of-sight, path loss, and absorption by building materials used in
construction [1]. With the advent of microcellular, radio networks employed in third-generation mobile
communication systems, there is an increased interest in propagation models that are able to provide location –
specific predictions of channel parameters such as local mean power, and delay spread [2].Propagation research
for mobile communications in urban microcells has hitherto been focused mainly on the modelling of reflection
and diffraction from exterior walls and corners of buildings. These buildings are usually treated as opaque at
frequencies used for terrestrial mobile communications. Radio propagation inside buildings is governed by
mechanisms such as reflection, diffraction, and scattering from various objects.field distribution inside a
building is therefore dependent on specific features of its internal structure (e.g. layout, construction materials)
[3]Propagation research can be done in both indoor and outdoor environments. Indoor and outdoor radio
channels differ largely in terms of the transmitter-receiver separation distance covered (which is usually much
smaller for indoor environments, and the variability of the environment is usually greater for indoor
environments). Propagation in indoor environments have somewhat, more complex multipath structure than in
outdoor environments which is largely due to the nature of the building structures used, the room layouts and the
type of materials used in the construction of the building.[4]In outdoor environment, propagation is affected by
obstacles within surroundings such as trees, buildings, and moving cars among others. While indoor
environment propagation on the other hand is affected by interior walls, metallic objects such as whiteboards,
bookcases, standing air conditioners and items of furniture.Since transmission and reception of signals can be
from outdoor to indoor environment, there is need to quantify the various contribution by these factors on the
signal strength. Quantifying the above mentioned effect is important so that the signal strength may be estimated
at the receiving end. Therefore, there is need to have the knowledge of degradation of the signal on its way to
the receiving end, in order to know the required transmitted power level at the transmission end.

Acomparative Study Between Modelled...
II. PROPAGATION MODELS
The free space propagation model is used to predict received signal strength when the transmitter and
receiver have a clear, unobstructed line of sight path between the
2.2 FREE SPACEPATH LOSS EXPONENT MODEL
The free propagation model predicts that the received power decays as a function of transmitter-
receiver raised to some exponent. The power received by the receiving antenna (pr) when placed at a distance (d,
in meters) from the transmitting antenna, which by the free space transmission equation as [2]
(1)
Where Gr and Gt are gains of the receiving and transmitting antenna respectively, λ is the wavelength in meters.
Large scale propagation models typically use a close-in distance as a reference distance and this is termed d0.
This value of pr (d0) is measured by taking the average received power at this distance from the transmitter. The
received power in free space at a distance greater than d0 is given by [2]
(2)
The reference distance (d0) is usually chosen such that it is smaller than any practical distance used in the
wireless communication system of interest, and also such that it lies in the far field region. If „n‟ represents the
path loss exponent, which characterizes the relationship between the increase in path loss values with increase in
transmitter- receiver separation distance, then path loss at a distance d (such that d > d0) can be expressed as [4]
(3)
The model relationship when plotted on a log-log scale is a straight line with a slope equal to 10ndB per decade
in distance. The value of „n‟ depends on specific propagation environment conditions. For instance the value of
the path loss exponent in free space is 2 and when obstructions are present and it usually has a larger values.
Researchers have estimated the path loss exponent for a loss case inside building can range from a value of 1.6
to 1.8 and when the path loss is obstructed, the exponent value increase to range of 4 to 6.
2.2 LOG-NORMAL SHADOWINNG MODEL
The log-normal shadowing model predicts path loss as a function of T-R separation using equation below [2]
(4)
Where Xσ is a zero-mean Gaussian random variable with standard deviation σ.
The random variable Xσ attempts to compensate for random shadowing effect that can result from clutter.
III. LINEAR REGRESSION ANALYSIS
This is a linear model which is use to examine the relationship between dependent and independent
variables. After performing an analysis on the data, regression statistic can be employed to predict the dependent
variable when the independent variable is known. This is represented by this equation below. [4]
Y=intercept+(slope)+loss (5)
The loss term in equation (5) is because the independent variable Xσ cannot fully accounts for all the observed
variation in the dependent variable. This loss is normally distributed. The task here is to determine regression
coefficients, a+bx in linear regression equation represented as yi=a+bx

Where „a‟ and „b‟ represent the intercept and the slope of the regression line respectively, yi is the fitted value of
y data and xi is the set of points X. We define the estimated loss associated with each pair of data values.
IV. RESULT AND DISCUSSION
The values of n and σ were determined from empirical data obtained from measurements through the
use of linear regression model.After determining the values of „n and σ as presented in table and were used to
calculate the theoretical received power considering log-normal shadowing model.results obtained from the
modelled and measured signal power and statistical analysis representing the values of path loss exponents „n‟
and standard deviation „σ’. The comprehensive evaluation on how these results were obtained using statistical
analysis and are presented table 1-5
The table 3 below shows the comparison between signal strength variations in mud building with rusty zinc roof
with other building types where measurements were conducted.
Linear regression using standard deviation to evaluate the value of “σ” in dB
Result obtained is presented in this table 5.0 below, Considering the speed of light in the study
λ2
= 3x10-8
/960 x106
= 0.096m, Gt=Gr= 1
and df =2D2
/λ =2(1.5)2
/0.3312=12.92m
(6)
Substituting the values in equation (6) we have
Pl (do) = -10log {1*1 *0.096 /(4 (12.92)2
}=54.39dB
(7)
Substituting the values in equation (7) we have
PL (d)=54.39 +1.5(10)log (12.92/12.92) +3.7=58.09dBm
Pr(dBm)=PtdBm)- PL (d) (8)
Pr (dBm)= 33.3-58.09= -24.79dBm
PL (do)= -10log (1*1*0.096)/ (4 (15.98)2
= 56.23dBm
PL (d)= 56.23 +15log(15.98.12.92)+3.7=66.21dBm and Pr(dBm)= 33.3-66.21= -32.91dBm
3. PL (do)= -10log (1*1*0.096)/ (4 (17.75)2
= 57.15dB
Pl (d) = 57.15+15log(17.75/12.92) +3.7=70.23dB and Pr (dBm)= 33.3-70.23= -.93dBm
pl (do)= -10log(1*1*0.096)/ (4 (19.01)2
=57.74dB
Pl (d)= 57.74+15log(19.01/12.92)+3.772.85dB
Pr (dBm)= 33.3-72.85
= -39.55dBm

Distance in meter „m‟
Fig 1 measured & modelled received power for Etisalat network kabala costain
Fig 2 measured & modelled received power for Etisalatnetwk kabala costain
Receivedpowerin„dBm‟

fig 3 measured & modelled received power for Airtel network Hayen-Danmani
fi4 measured & modelled received power for Mtn network Hayen-Danmani.

Table1 SIGNAL STRENGTH MEARSURED (DBM)
S/No indoor outdr Diffrnc. Avrg
Airtel
1 -50.39 -45.08 -5.31
2 -54.19 -46.14 -8.05 -7.57
3 -50.13 -40.18 -9.35
MTN
1 -48.18 -44.67 -3.51
2 -38.09 -31.56 -6.53 -5.59
3 -40.16 -33.23 -6.73
Glo
1 -44.31 -35.01 -9.30
2 -43.44 -39.07 -4.37 -5.54
3 -39.24 -36.29 -2.95
Starcom
1 -58.03 -41.59 -7.56
2 -59.12 -40.37 -4.74 -8.04
3 -55.53 -42.05 -2.79
Etisalat
1 -50.04 -41.59 -8.45
2 -53.08 -40.37 -12.71 -5.03
3 -55.53 -42.05 -2.95

Table 2 HAYEN-DANMANI AIRTEL NETWORK
Distnc(
m)
10log(d)xi
in „m‟
Power
receivedyi‟
xiyi Xi
2
16.4 12.15 -29 -352 147.6
36.4 15.61 -32 -499 243.6
56.4 17.51 -36 -630 306.6
76.4 18.83 -37 -696 354.5
96.4 19.84 -38 -753 393.6
116.4 20.66 -41 -847 426.8
136.4 21.35 -43 -918 455.8
Table 3 HAYIN DANMANI AIRTEL NTwK
S/N Distance
(m)
Measured
Received
Power(dB
Modeled
Received
Power(dBm
1 12.15 -29 -22.29
2 15.61 -32 -31.87
3 17.51 -36 -36.26
4 18.83 -37 -39.04
5 19.84 -38 -41.04
6 20.66 -41 -42.58
7 21.35 -43 -43.84

Table 4 HAYIN - DANMANI MTN NETWORK
S/N Distance in (m) Measured Received Power(dBm Modelled
Received Power(dBm)
1 12.65 -30 -24.62
2 15.84 -34 -33.12
3 17.66 -36 -37.23
4 18.44 -38 -39.87
Table 5 HAYIN - DANMANI MTN NETWORK
X
( - )
12.65 -12.01 145.68
15.84 -8.88 78.85
17.66 -7.06 49.84
18.44 -5.78 33.41
19.93 -4.79 22.94
20.73 -3.99 15.92
REFERENCES
[1] Georgia Institute of Technology, smart Antenna Research/laboratory
[2] Theodore S. Rappaport “wireless communications principles and practice” second edition, PHI learning private limited New
delhi-110001 2009, pp (105-177).
[3] N. Yarkoni and N. Blaunstein “prediction of propagation characteristic in indoor radio communication environment” progress in
electromagnetic research, PIER59, 151-174 2006
[4] J.D Parsons, “The mobile radio propagation channel second edition copyright 2000 John Wiley and son Ltd print ISBN 0-471-
98857-x pp190-195.
[5] S.C Gupta „Fundamentals of Statistics‟ Himalaya publishing house PVT. Ltd six editions 20011 (pp9.10).
[6] Amaefule C.S 2003 “The GSM communication explosion in Nigeria” proceedings of the NCS conference 2003.14:163-182

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E03702038045