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\title{Path Loss Prediction for Some GSM Networks for Akwa Ibom State, Nigeria}
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             \author[1]{Michael U.  Onuu}

             \affil[1]{  University of Uyo, Uyo, Nigeria}

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\date{\small \em Received: 13 December 2016 Accepted: 5 January 2017 Published: 15 January 2017}

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\begin{abstract}
        


Path loss prediction for some Global System of Mobile Communication (GSM) networks for Akwa Ibom State in the Federal Republic of Nigeria was undertaken in this study in order to obtain a suitable path loss model for path loss prediction for the State. Received Signal Strength (RSS) and path loss were obtained from MTN and GLO base stations (networks) located in Uyo, Eket, Ikot-Ekpene, Onna, Etinan and Oruk-Anam which are some major towns in Akwa Ibom State. Path loss plots from theoretical models and experimental data against Basic Transceiver System (BTS), mobile device distance, gave positive linear relationships resulting in the proposed path loss model for Akwa Ibom State. Comparative analysis of Mean Square Error (MSE) obtained showed Hata model to be the most reliable and suitable path loss prediction model for Akwa Ibom State. The MSE value for each town was 5.9dB, 4.09dB, 5.93dB and 4.03dB for Uyo, Eket, Onna and Etinan, respectively. It was found that Egli model with MSE value of 5.97dB is suitable for path loss prediction in Ikot- Ekpene due to its irregular terrain. Results also showed that none of the models being considered gave acceptable MSE value for Oruk-Anam. In this case, the proposed path loss prediction model for Akwa Ibom State should be used for Oruk-Anam. The results of this investigation therefore lend credence to the fact that terrain and infrastructural development affect not only path loss, but also the suitability of a given path loss model for a particular environment.

\end{abstract}


\keywords{path loss, global system of mobile communication (GSM), base transceiver station (BTS), received signal strength (RSS), networks and akwa ibom state.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction a) Overview}]{Introduction a) Overview}\par
ince the advent of telecommunication, there have been researches on how to improve and enhance communication between people at various locations. This resulted in Global System for Mobile Communication (GSM) which is a wireless form of communication that propagates information (voice and data) in the form of an electromagnetic (EM) wave.\par
It is a fact that cellular phones have revolutionized personal communications for millions of people around the globe. Like any mobile radio, a cellular phone transmits and receives electromagnetic reflection and absorption of radio energy by buildings is high, thus loss in power density will be high.\par
According to Mawjoud \hyperref[b6]{[6]}, networking planning is vital in the prediction of path loss and hence the coverage area, frequency assignment and interference which are the main concerns in mobile network planning. The available empirical formulae cannot be generalized to different environments (urban, sub-urban, rural). In general, suitability of these models differ for different environment.\par
Several propagation models have been formulated for prediction of path loss, but due to difference in terrain and level of development of a particular environment, appropriate model for a particular environment differs. This study is aimed at obtaining a propagation model that is suitable, reliable and most accurate for path loss prediction in an environment and terrain like Akwa Ibom State in the Federal Republic of Nigeria. 
\section[{II.}]{II.} 
\section[{Review Of Previous Works}]{Review Of Previous Works}\par
Path loss is the gradual reduction in power density of an electromagnetic wave as it propagates through the space from a source. Electromagnetic wave propagates through space from one region to another even when there is no matter in the intervening region. Electromagnetic wave, when traveling through an unguided medium, undergoes different kinds of propagation effects such as reflection, diffraction, free space loss, absorption, aperture medium, coupling loss and scattering. These propagation effects are the causes of reduction in power density (path loss). Path loss is as a result of received signal becoming weaker due to increasing distance between the base station and the transceiver system. This occurs even when there are no obstacles between the transmitting antenna and the receiving antenna. Radio wave propagation through a city is greatly affected depending on whether there is line-of-sight (LOS) between transmitting and receiving antennae or not. This is because propagation characteristics of the radio wave, such as path loss, fading and attenuation do not only depend on the distance and frequency, but also on the scatter angle that depends on what is causing the obstruction to the propagated wave \hyperref[b13]{[13]}.\par
A number of researchers have worked on path loss prediction which is of vital importance in GSM network design, planning, location of BTS, coverage area, frequency assignment and interference for effective cellular networks aimed at achieving effective signal values and levels between a transceiver and a mobile device.\par
Mawjoud \hyperref[b6]{[6]} in his work on path loss propagation model prediction for GSM network planning studied the outdoor path loss behavior in Mosul city in Iraq to predict a suitable propagation model at the frequencies of 900MHz and 1800MHz in urban and sub-urban environments. After comparing the empirical models such as Hata, costs-231 Hata, International Telecommunication Union -Radio (ITU-R), Ericson and Stanford University Interim (SUI) with the experimental measured path loss for urban areas in Mosul city, the result showed that at 900MHz frequency, the best fit model for urban and sub-urban is Hata and Ericson models and for 1800MHz frequency, the best fit model for industrial and sub-urban areas is the Costa-Hata model. This shows that every environment has its distinctive characteristic factors and features that affect the propagation of wave differently. This, thus, precludes the generalization of a particular model for different environment. Various works \hyperref[b10]{[10,}\hyperref[b11]{11]} also showed that a path loss model cannot be generalized for different environment.\par
Also Isabona and Konyeha \hyperref[b5]{[5]} in their study on urban area path loss propagation prediction and optimization using Hata model at 800MHz showed how Okumura Hata model is chosen and optimized for urban outdoor coverage in the Code Division Multiple Access (CDMA) system operating in 800MHz UHF frequency band in South South Nigeria. They compared measured path loss with theoretical path loss obtained from Hata, SUI, Lee and Egli models. In their result, Hata model was the nearest in agreement with the measured values. Based on these, they developed an optimized Hata model for the prediction of path loss experienced by CDMA 2000 signal in 800MHz band. 
\section[{a) Reasons and causes of path loss}]{a) Reasons and causes of path loss}\par
The reduction in power density (path loss) of a signal as it propagates from a source is caused by various factors which includes free space loss, diffraction, multipath fading, buildings and vegetation, terrain and atmosphere. 
\section[{b) Theoretical path loss models}]{b) Theoretical path loss models}\par
Theoretical models were derived based on the physical laws of wave propagation \hyperref[b10]{[10]}. The theoretical path loss prediction models are divided into two basic types, namely; free space path loss model and plane earth propagation model. 
\section[{i. Free space propagation model}]{i. Free space propagation model}\par
In free space, the wave is not reflected or absorbed. Ideal propagation implies equal radiation in all direction from the radiating source and propagates to an infinite distance with no degradation. The free space path loss model is used to predict received signal strength when the transmitter and receiver have a clear unobstructed line-of-sight, LOS, path between them \hyperref[b10]{[10]}. In satellite communication, microwave in LOS radio links typically undergo free propagation. According to \hyperref[b1]{[2,}\hyperref[b8]{8,}\hyperref[b10]{10]}, the power flux is given by f d dB L P + + = 2.6\par
where f is the carrier frequency in MHz, d is the T-R distance in km. 
\section[{ii. The plane earth model}]{ii. The plane earth model}\par
According to \hyperref[b14]{[14,} {\ref 18]}, path loss experience is worse in terrestrial environment than in free space. The most significant difference between terrestrial environment and free space is the presence of ground (and ground reflection) in a terrestrial environment. The plane earth loss increases far more rapidly than the free space loss and it is independent of carrier frequency. In plane earth model \hyperref[b14]{[14,} {\ref 18]} The plane earth loss is rarely an accurate model of real-world propagation when taken in isolation. It only holds for long distance and for cases where the amplitude and phase of the reflected wave is very close to the idealized in case a equals 1. 
\section[{c) Empirical Models}]{c) Empirical Models}\par
Empirical models, also known as stochastic models, are models obtained from experimental observation. There are of various types and their suitability differs with respect to terrain. In this work, Okumura model, Hata model, Cost-231 model and Egli model will be discuss.\par
i. Okumura Model According to \hyperref[b10]{[10]}, the Okumura's model is an empirical model based on extensive drive test measurements made in Japan at several frequencies within the range of 150 to 1920 MHz, but is extrapolated to 3000 MHz. For Okumura model, the prediction area is divided into terrain categories; open areas, suburban area and urban area \hyperref[b15]{[15]}. Nadir and Ahmad showed that the signal strength decreases at much greater rate with distance than that predicted by free space model \hyperref[b7]{[7]}.\par
Okumura developed a set of curves giving the median attenuation relation to free space (?? ???? ), in an urban area over a quasi-smooth terrain with a base station effective antenna height The empirical path loss formula of Okumura is expressed as \hyperref[b10]{[10,}\hyperref[b15]{15]}  ( ) ii. Hata Model The Hata model is an empirical formulation of the graphical path loss data provided by Okumura model \hyperref[b4]{(Hata, 1980)}. It is valid over roughly the same range of frequencies 150MHz to 1500MHz. This empirical formula simplifies the calculation of path loss because it is closed form formula and it is not based on empirical curves for different parameters. Two forms of the Okumura-Hata model are available  {\ref [20]}. In the first form, the path loss (in dB) is written as The more common form is a curve fitting of Okumura's original result. In that implementation, the path loss is written as  {\ref [} AREA m b mu F G h G h G d f A L dB L ? ? ? + = ) ( ) ( ) , ( ) (m h m h h G b b b 1000 30 : 200 log 20 ) ( 10 < < = 2.11a ( ) m h h h G m m m 3 : 3 log 10 ) ( 10 < = 2.11b ( ) m h m h h G m m m 10 3 : 3 log 20 ) ( 10 ? ? = 2.11ccm cb exc space free H H A PL PL ? ? + = 2.12m b c h a h f A ? ? + = 2.14a ( ) ( ) ? ? ? = ? ? ? = 0 8 . 0 ) log( 56 . 1 7 . 0 ) ( log 1 . 1 ) ( C f h f h a c m c m 2.14c\par
For metropolitan areas or large cities Path Loss Prediction for Some GSM Networks for Akwa Ibom State, Nigeria © 2017 Global Journals Inc. (US) ? ? ? ? ? = ? ? ? ? ? ? ? ? ? = 0 400 97 .\textbf{4}? ? ? ? = m b h h d L 2.20 III. 
\section[{Cell, BTS And Mobile Device}]{Cell, BTS And Mobile Device}\par
Global system for mobile communication (GSM) is made up of a BTS and a mobile device enclosed within a cell.\par
In GSM, a cell is the geographical area covered by radio frequency from BTS which a mobile device located within that range can connect reliably is the transceiver (Figure  {\ref 2}.1). The size of a cell is not fixed, it depends on several factors such as line-of-sight, reflection and absorption of radio frequency by obstacles and vegetation, height of the antenna, transmitters rate power, the required uplink/down link data rate of the subscribers device and the terrain. 
\section[{iv. Egli Model}]{iv. Egli Model}\par
Egli model is an irregular terrain model for radio frequency propagation \hyperref[b10]{[10,}\hyperref[b15]{15]}. Egli model provides the median path loss due to terrain loss. It predicts the total path loss for point-to-point link (link-of-sight transmission). Typically, it is suitable for cellular communication scenarios where one antenna is fixed and another is mobile. Egli model is expressed as \hyperref[b15]{[15]} ? 2 2 50 ? ? ? ? ? ? = d h h G G L m b m b 2.18 2 40 ? ? ? ? ? ? ? ? = f ? 2.19\par
where f is the frequency in MHz combing equation 2.18 and 2.19, Egli model is given by2 2 2 50 40 ? ? ? ? ? ? ? ? ? ? ? ? ? ? = f d h h G G L m b m b\par
The gain for mobile station, m  
\section[{D}]{D}\par
Sharma and Singh showed that these cells joined together to provide radio coverage over a large geographical area \hyperref[b16]{[16]}. Path loss determines the cell range. For GSM, there are three cell ranges. Table  {\ref 2}.4. Hamad-ameen from his research showed that the accuracy of cell planning depends on several factors and accuracy of propagation model is one of them \hyperref[b3]{[3]}. Base Transceiver Station (BTS) in mobile communications holds the radio transceiver that defines a cell and co-ordinate the radio-link protocols with the mobile device. The BTS is the networking component of a mobile communications system from which all signals are sent and received. Thus it facilitates wireless communication between a device and network thereby creating the cell in a cellular network. A BTS consist of the following: antennas that relay radio message, transceivers, duplexers and amplifiers while a mobile device is a portable, wireless computing device that is small enough to be used while held in the hand; a handheld. These include mobile phones, PDA, computers. 
\section[{Fig. 2.1: cell}]{Fig. 2.1: cell}\par
A mobile phone operates on a cellular network which is composed of cells. If a subscriber (user) is located outside the cell belonging to the cellular network provider the user subscribed to, such a user cannot place or receive calls in that location. 
\section[{a) Experimental Design}]{a) Experimental Design}\par
The methods employed in this study include physical site survey, collection of data, GPS measurement and analysis (graphs and regression). A detailed field study exercise for collection of data was carried out in selected cities of Akwa Ibom State using a mobile phone.\par
A NET monitor software installed in a Samsung galaxy phone was used to obtain the received signal strength from a fixed BTS at selected locations while GPS was used to measure the BTS -mobile device distance while a Personal Computer (PC) was used to save the collected data.\par
This study was conducted in December, 2015 in selected cities of Akwa Ibom State at a temperature of 27 o C. The Local Government Areas in which the investigation was carried out were Uyo, Eket, Ikot-Ekpene, Onna, Etinan and Oruk-Anam (Table  {\ref 3.1}  
\section[{c) Receiver Signal Strength (RSS)}]{c) Receiver Signal Strength (RSS)}\par
In telecommunications, Received Signal Strength is the power present in a received radio signal and it is express in decibel (dB).\par
Below is a range of signal strength and its effect on quality of service.  
\section[{Results And Discussion}]{Results And Discussion}\par
The empirical path loss result was evaluated using four different path loss models, namely, free space model,  {\ref Hata}  V. 
\section[{Experimental Result}]{Experimental Result}\par
The collected measurement for MTN and GLO bass stations for the selected cities are shown below.     
\section[{3.3}]{3.3}\par
The proposed path loss model will be given by ?? ?? = ?? + ?? 0 ?? ???? + ?? 3. 
\section[{4.7}]{4.7}\par
The Mean Square Error (MSE) compares the measured data with the data obtained from each of the empirical models to determine the minimum MSE. The model that gives the least MSE and also not greater than 6dB, the minimum value of Mean Square Error for good signal propagation is suitable for prediction of path loss in the area in consideration. The Mean Square Error is expressed as?????? = ? (?? ?? ? ?? ?? ) 2 ??\par
where P M is the measured value, P E is the empirical value and N is the values of data taken.\par
For Uyo  
\section[{Discussion}]{Discussion}\par
The results of path loss obtained from four empirical models are shown in table 4.1.The data shows that free space model has least path loss followed by Egli model and then Hata and COST-231 model which has close values.\par
The experimental results of received signal strength and path loss measured are shown in table 4.2 to 4.7. Regression analysis carried out on the results of Path Loss Prediction for Some GSM Networks for Akwa Ibom State, Nigeria each location gives equation 4.1 to 4.6. Figure  {\ref 4}.1 to 4.6 show plots of path loss in decibel against distance in kilometres for the six study area. The graph shows a linear relationship between path loss and distance, increase in distance led to increase in path loss.\par
The MSE compares the measured data with the data obtained from each of the empirical model to determine the minimum MSE. The model that gives the least MSE and also not greater than 6dB, the minimum value of MSE for good signal propagation is suitable for prediction of path loss in the area in consideration. From the evaluation, MSE value obtained for Hata model (5. 9dB, 4.09dB, 5.93dB, 4.03dB) for Uyo, Eket, Onna and Etinan LGA respectively falls within the acceptable values of MSE for good signal propagation while Egli model (5.97) for Ikot-Ekpene is the acceptable value. From the evaluation, the least MSE value for Oruk-© 2017 Global Journals Inc. (US) Anam, Hata model (7.44) is above the minimum MSE value of 6db for a good signal propagation. 
\section[{VII.}]{VII.} 
\section[{Conclusion}]{Conclusion}\par
From the investigation, Hata model has the minimum means square error (MSE) of 5.9dB, 4.09dB, 5.93dB and 4.03dB for Uyo, Eket, Onna and Etinan, respectively. These values fall within the acceptable value of minimum MSE of 6dB for a good signal propagation. Hata model is more reliable and suitable for accurate path loss prediction in these areas while Egli model with MSE value of 5.97db for Ikot-Ekpene is suitable for path loss prediction for Ikot-Ekpene. This investigation also shows that the least MSE value of 7.44db for Oruk-Anam was obtained from Hata model but it is greater than the minimum MSE of 6dB for a good signed propagation. In these cases the proposed model (???? = 116.38 + 6.3?? ???? ) obtained from this study can be used Oruk-Anam.\par
From this study, Hata model gives a fairer result for path loss prediction for Akwa Ibom State. The study also shows that no generic model is suitable for generalized used since each model differs in their applicability over different terrain. For effective path loss prediction in Akwa Ibom State and network coverage performance, the proposed path loss model in equation 4.7 obtained from the experimental results from the state is reliable, suitable and more accurate.\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_3}G}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
    \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0038288288288288288\textwidth}P{0.0057432432432432436\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0038288288288288288\textwidth}P{0.0076576576576576575\textwidth}P{0.5283783783783783\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0038288288288288288\textwidth}P{0.16846846846846847\textwidth}P{0.0057432432432432436\textwidth}P{0.0019144144144144144\textwidth}P{0.0019144144144144144\textwidth}P{0.0038288288288288288\textwidth}P{0.011486486486486487\textwidth}P{0.0057432432432432436\textwidth}P{0.0057432432432432436\textwidth}P{0.0019144144144144144\textwidth}P{0.015315315315315315\textwidth}P{0.03445945945945946\textwidth}P{0.0076576576576576575\textwidth}P{0.009572072072072071\textwidth}P{0.0057432432432432436\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep L\tabcellsep \multicolumn{3}{l}{p =}\tabcellsep 2 d t h\tabcellsep 4 h r 2\tabcellsep 2.7\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{14}{l}{where d is the distance (in metres) between the}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{14}{l}{transmitter and receiver, t h is the height (in metres) of}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{14}{l}{the transmitter antenna and r h is the height (in metres)}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{11}{l}{of the receiver antenna.}\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{10}{l}{In practice, a correction factor a is added to}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{9}{l}{equation 2.7 to yield}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{L}\tabcellsep \multicolumn{3}{l}{p =}\tabcellsep 2 d ah t\tabcellsep 4\tabcellsep 2 r h\tabcellsep 2.8\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{10}{l}{The correction factor a depends on the}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{14}{l}{frequency of the carrier. Converting equation 2.8 to}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{6}{l}{decibel gives.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
L\tabcellsep p\tabcellsep =\tabcellsep 10\tabcellsep log\tabcellsep (\tabcellsep a\tabcellsep )\tabcellsep +\tabcellsep 20\tabcellsep log(\tabcellsep h t\tabcellsep )\tabcellsep +\tabcellsep 20\tabcellsep log(\tabcellsep h r\tabcellsep )\tabcellsep ?\tabcellsep 40\tabcellsep \multicolumn{3}{l}{log(}\tabcellsep d\tabcellsep )\tabcellsep 2.9\end{longtable} \par
 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{21} \par 
\begin{longtable}{P{0.4885826771653543\textwidth}P{0.05354330708661417\textwidth}P{0.3078740157480315\textwidth}}
Carrier frequency\tabcellsep F\tabcellsep 150 to 1920 MHz\\
Base station antenna height\tabcellsep b h\tabcellsep 30 to 1000m\\
Mobile antenna height\tabcellsep m h\tabcellsep 1 to 10m\\
Distance\tabcellsep D\tabcellsep 1km to 100km\end{longtable} \par
 
\caption{\label{tab_2}Table 2 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{23} \par 
\begin{longtable}{P{0.1326151560178306\textwidth}P{0.0012630014858841009\textwidth}P{0.0012630014858841009\textwidth}P{0.0025260029717682018\textwidth}P{0.011367013372956909\textwidth}P{0.0025260029717682018\textwidth}P{0.008841010401188707\textwidth}P{0.0063150074294205044\textwidth}P{0.0025260029717682018\textwidth}P{0.0025260029717682018\textwidth}P{0.08714710252600297\textwidth}P{0.0012630014858841009\textwidth}P{0.0050520059435364036\textwidth}P{0.0037890044576523027\textwidth}P{0.0012630014858841009\textwidth}P{0.0025260029717682018\textwidth}P{0.031575037147102525\textwidth}P{0.07199108469539377\textwidth}P{0.10104011887072807\textwidth}P{0.008841010401188707\textwidth}P{0.10609212481426449\textwidth}P{0.016419019316493314\textwidth}P{0.20208023774145614\textwidth}P{0.0050520059435364036\textwidth}P{0.011367013372956909\textwidth}P{0.010104011887072807\textwidth}P{0.0012630014858841009\textwidth}P{0.0037890044576523027\textwidth}P{0.0012630014858841009\textwidth}P{0.0063150074294205044\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep B\tabcellsep =\tabcellsep 44\tabcellsep .\tabcellsep 9\tabcellsep ?\tabcellsep 6\tabcellsep .\tabcellsep 55\tabcellsep log\tabcellsep (\tabcellsep b h\tabcellsep )\tabcellsep 2.14b\\
\multicolumn{17}{l}{where c f is given in MHz and d in km,}\tabcellsep a\tabcellsep ( m h\tabcellsep )\tabcellsep is a correction factor for mobile antenna height.\\
\multicolumn{5}{l}{The function}\tabcellsep \multicolumn{2}{l}{a}\tabcellsep ( m h\tabcellsep \multicolumn{2}{l}{)}\tabcellsep \multicolumn{7}{l}{and C depend on the environment for small and medium-size cities.}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{a}\tabcellsep \multicolumn{2}{l}{(}\tabcellsep \multicolumn{2}{l}{h m}\tabcellsep \multicolumn{2}{l}{)}\tabcellsep 75 54 . . 11 (log( 2 1 ( (log 29 . . 3 8 h h m m\tabcellsep )) )) 2 2\tabcellsep . 1\tabcellsep 1\tabcellsep for for\tabcellsep f f c c\tabcellsep 200\tabcellsep MHz MHz\tabcellsep 2.14d\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{C}\\
\multicolumn{8}{l}{For suburban environment}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep The function\tabcellsep a\tabcellsep ( m h\tabcellsep )\tabcellsep in suburban and rural area\\
\multicolumn{2}{l}{C}\tabcellsep \tabcellsep =\tabcellsep \multicolumn{4}{l}{[ log( 2}\tabcellsep f\tabcellsep c\tabcellsep \multicolumn{2}{l}{/}\tabcellsep \multicolumn{4}{l}{) 28}\tabcellsep ] 4 . 5 2 ?\tabcellsep 2.14e\tabcellsep is the same as urban (small and medium-size cities) areas.\\
For rural area log( 78 . 4 = C\tabcellsep f\tabcellsep c\tabcellsep )\tabcellsep 2\tabcellsep ?\tabcellsep \multicolumn{4}{l}{33 . 18}\tabcellsep \multicolumn{4}{l}{log}\tabcellsep \multicolumn{2}{l}{(}\tabcellsep f\tabcellsep c\tabcellsep )\tabcellsep ?\tabcellsep 98 . 40\tabcellsep 2.14f\tabcellsep The Hata model will approximate the Okumura model for distance 1 > km. Hence, it is a good d model for first generation cellular system, but it does not\end{longtable} \par
 
\caption{\label{tab_4}Table 2 . 3}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{24} \par 
\begin{longtable}{P{0.37\textwidth}P{0.48\textwidth}}
Cell\tabcellsep Cell Radius\\
Large cells\tabcellsep 1km ? r ? 30km\\
Small cells\tabcellsep 1km to 30km\\
Micro cells\tabcellsep 200m to 300m\end{longtable} \par
 
\caption{\label{tab_5}Table 2 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.4741035856573705\textwidth}P{0.3758964143426295\textwidth}}
Signal Strength (dB)\tabcellsep Quality of\\
?105 ???? ? 100\tabcellsep Bad/drop call\\
? 99 ???? ? 90\tabcellsep Getting bad/signal may break up\\
\multicolumn{2}{l}{? 89 ???? ? 80 Quality of service should not have problem}\\
? 79 ???? ? 65\tabcellsep Quality of service is good\\
???????????? ? 65\tabcellsep Quality of service is excellent\\
IV.\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2017\\
20\\
II Version I\\
Journal of Researches in Engineering ( ) Volume XVII Issue D\\
Global\end{longtable} \par
 
\caption{\label{tab_7}Table 3 . 1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.1233160621761658\textwidth}P{0.1893782383419689\textwidth}P{0.23341968911917096\textwidth}P{0.13212435233160622\textwidth}P{0.1717616580310881\textwidth}}
Distance (km)\tabcellsep Free space (dB)\tabcellsep \multicolumn{2}{l}{Hata (dB) Cost-231 (dB)}\tabcellsep Egli (dB)\\
1.0\tabcellsep 91.58\tabcellsep 123.97\tabcellsep 123.59\tabcellsep 110.46\\
2.0\tabcellsep 97.61\tabcellsep 133.95\tabcellsep 133.84\tabcellsep 122.50\\
3.0\tabcellsep 101.13\tabcellsep 139.79\tabcellsep 139.84\tabcellsep 129.35\\
4.0\tabcellsep 103.63\tabcellsep 143.65\tabcellsep 144.10\tabcellsep 134.54\\
5.0\tabcellsep 105.56\tabcellsep 146.86\tabcellsep 146.86\tabcellsep 138.42\end{longtable} \par
 
\caption{\label{tab_8}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.13453237410071942\textwidth}P{0.2079136690647482\textwidth}P{0.29964028776978413\textwidth}P{0.2079136690647482\textwidth}}
\multicolumn{2}{l}{Network Distance}\tabcellsep \multicolumn{2}{l}{RSS (dB) Path loss}\\
\tabcellsep (km)\tabcellsep \tabcellsep (dB)\\
MTN\tabcellsep 1.0\tabcellsep -71\tabcellsep 118\\
\tabcellsep 2.0\tabcellsep -78\tabcellsep 125\\
\tabcellsep 3.0\tabcellsep -81\tabcellsep 128\\
\tabcellsep 4.0\tabcellsep -89\tabcellsep 136\\
\tabcellsep 5.0\tabcellsep -97\tabcellsep 144\\
GLO\tabcellsep 1.0\tabcellsep -79\tabcellsep 126\\
\tabcellsep 2.0\tabcellsep -85\tabcellsep 132\\
\tabcellsep 3.0\tabcellsep -89\tabcellsep 136\\
\tabcellsep 4.0\tabcellsep -95\tabcellsep 142\\
\tabcellsep 5.0\tabcellsep -101\tabcellsep 148\end{longtable} \par
 
\caption{\label{tab_9}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{P{0.1689274447949527\textwidth}P{0.0804416403785489\textwidth}P{0.2198738170347003\textwidth}P{0.1608832807570978\textwidth}P{0.10457413249211356\textwidth}P{0.11529968454258674\textwidth}}
\tabcellsep \tabcellsep \multicolumn{2}{l}{Network Distance (km)}\tabcellsep RSS (dB)\tabcellsep Path loss(dB)\\
\tabcellsep \tabcellsep MTN\tabcellsep 1.0\tabcellsep -83\tabcellsep 130\\
\tabcellsep \tabcellsep \tabcellsep 2.0\tabcellsep -91\tabcellsep 138\\
\tabcellsep \tabcellsep \tabcellsep 3.0\tabcellsep -97\tabcellsep 144\\
\tabcellsep \tabcellsep \tabcellsep 4.0\tabcellsep -99\tabcellsep 146\\
\tabcellsep \tabcellsep \tabcellsep 5.0\tabcellsep -107\tabcellsep 154\\
\tabcellsep \tabcellsep GLO\tabcellsep 1.0\tabcellsep -79\tabcellsep 126\\
\tabcellsep \tabcellsep \tabcellsep 2.0\tabcellsep -83\tabcellsep 130\\
\tabcellsep \tabcellsep \tabcellsep 3.0\tabcellsep -91\tabcellsep 138\\
\tabcellsep \tabcellsep \tabcellsep 4.0\tabcellsep -95\tabcellsep 142\\
\tabcellsep \tabcellsep \tabcellsep 5.0\tabcellsep -97\tabcellsep 144\\
\multicolumn{4}{l}{Table 4.7: Measurement for Oruk Anam}\\
\multicolumn{2}{l}{Network Distance (km)}\tabcellsep \multicolumn{2}{l}{RSS (dB) Path loss (dB)}\\
MTN\tabcellsep 1.0\tabcellsep -83\tabcellsep 131\\
\tabcellsep 2.0\tabcellsep -92\tabcellsep 140\\
\tabcellsep 3.0\tabcellsep -98\tabcellsep 146\\
\tabcellsep 4.0\tabcellsep -107\tabcellsep 155\\
\tabcellsep 5.0\tabcellsep -113\tabcellsep 161\\
GLO\tabcellsep 1.0\tabcellsep -75\tabcellsep 122\\
\tabcellsep 2.0\tabcellsep -81\tabcellsep 128\\
\tabcellsep 3.0\tabcellsep -87\tabcellsep 134\\
\tabcellsep 4.0\tabcellsep -91\tabcellsep 138\\
\tabcellsep 5.0\tabcellsep -99\tabcellsep 144\end{longtable} \par
 
\caption{\label{tab_10}Table 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.06231466227347611\textwidth}P{0.5314250411861614\textwidth}P{0.009802306425041186\textwidth}P{0.009802306425041186\textwidth}P{0.004901153212520593\textwidth}P{0.02730642504118616\textwidth}P{0.04761120263591433\textwidth}P{0.1281301482701812\textwidth}P{0.014003294892915978\textwidth}P{0.014703459637561778\textwidth}}
\tabcellsep \multicolumn{5}{l}{Path Loss Prediction for Some GSM Networks for Akwa Ibom State, Nigeria}\\
\tabcellsep For Ikot-Ekpene\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{2}{l}{?? ?? = 10}\tabcellsep \multicolumn{3}{l}{(4060) 10(110)}\tabcellsep ? 30\tabcellsep (1305) (30)2\\
\tabcellsep \multicolumn{4}{l}{?? ?? = 7.25}\\
\tabcellsep ?? =\tabcellsep \multicolumn{3}{l}{1305 10}\tabcellsep ? 7.25\tabcellsep (3??) 10\\
\tabcellsep \multicolumn{5}{l}{?? = 108.75}\\
\tabcellsep \multicolumn{5}{l}{???? = 108. 75 + 7. 25?? ????}\tabcellsep 4.3\\
\tabcellsep For Onna\tabcellsep \tabcellsep \tabcellsep \\
Year 2017\tabcellsep \multicolumn{5}{l}{?? ?? = 10 ?? ?? = 6.8 10(110) (4099)}\tabcellsep ? 30\tabcellsep (1321) (30)2\\
\tabcellsep ?? =\tabcellsep \multicolumn{3}{l}{1321 10}\tabcellsep ? 6.8\tabcellsep (30) 10\\
II Version I ( ) Volume XVII Issue D Journal of Researches in Engineering Global\tabcellsep \multicolumn{5}{l}{?? = 111.7 ???? = 111. 7 + 6. 8?? ???? ?? ?? = 10 10(4280) 10(110) ? 30 ?? ?? = 5.2 ?? = 1392 10 ? 5.2 (3??) 10 = 123.6 ???? = 123.6 + 5.2?? ???? For Oruk-Anam For Etinan The equation of the line of regression of Y on X 4.4 (1392) (30)2 4.5 is given as ?? ?? = 10(4336) 10(110) ? 30 (1401) (30)2 ?? ?? = 6.65 ?? = 1401 10 ? 6.65 (3??) 10 ?? = 120.15 ???? = 120. 15 + 6. 65?? ???? 4.6 The proposed path loss model for Akwa Ibom State is inducted as follows: 60(25133) ?? ?? = 60(660) (8124) ? 180 (180)2}\tabcellsep For Eket\tabcellsep ?? ?? = 10 ?? ?? = 5.85 (4122) 10(110) ?? = 1335 10 ? 5.85 ? 30 (3??) (1325) (30)2 10 ?? = 11 5.95 ???? = 11 5. 95 + 5. 85 ?? ????\\
\tabcellsep \multicolumn{5}{l}{?? ?? = 6.34}\tabcellsep ?? ?? = 10\tabcellsep (4253) 10(110)\tabcellsep ? 30\tabcellsep (30)2 (1379)\\
\tabcellsep \multicolumn{3}{l}{?? =}\tabcellsep \multicolumn{2}{l}{8124 60}\tabcellsep ? 6.34\tabcellsep (180) 60\tabcellsep ?? ?? = 5.8\\
\tabcellsep \multicolumn{5}{l}{?? = 116.34}\tabcellsep ?? =\tabcellsep 1379 10\tabcellsep ? 5.8\tabcellsep (3?? ) 10\\
\tabcellsep \multicolumn{5}{l}{???? = 116. 38 + 6. 34?? ????}\tabcellsep ?? = 12 0.5\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ???? = 120. 5 + 5. 8 ?? ????\tabcellsep 4.2\end{longtable} \par
 
\caption{\label{tab_11}}\end{figure}
 			\footnote{Path Loss Prediction for Some GSM Networks for Akwa Ibom State, Nigeria © 2017 Global Journals Inc. (US)} 			\footnote{© 2017 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[ International Journal of Applied Information System (IJAIS)]{b2}\label{b2} 	 		\textit{},  	 	 		\textit{International Journal of Applied Information System (IJAIS)}  		2  (7) .  	 
\bibitem[Saunder and Aragon-Zarala ()]{b14}\label{b14} 	 		\textit{Antennas and Propagation for Wireless Communication System},  		 			S Saunder 		,  		 			A Aragon-Zarala 		.  		2007. Wiley Publishers. p. .  	 	 (2nd ed.) 
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\end{bibitemlist}
 			 		 	 
\end{document}
