Radio Wave Propagation Path Loss Modelling in Mobile Communications Environment: a Case Study of Ogun-state in Nigeria

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JOURNAL OF TELECOMMUNICATIONS, VOLUME 20, ISSUE 1, MAY 2013

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Radio Wave Propagation Path LossModelling in Mobile Communications

Environment: a Case Study of Ogun-state inNigeriaIbikunle Frank and SomoyeAbiodun

Abstract—Propagation models are used extensively in network planning, particularly for conducting feasibility studies and

during initial deployment. This research work sets out to determine the propagation model that is best suitable for Ogun-State in

Nigeria. Accurate characterization of radio propagation channel through important parameters and a mathematical model is

important for predicting signal coverage, achievable data rates, specific performance attributes of signaling and reception

schemes. This can be achieved by modifying the Okumura-Hatamodel which has wide acceptability and is currently in use for

mobile radio propagation to suit the terrain of the investigated regions in Nigeria. The Okumura- Hata’s equation for predicting

signal path loss was simulated.From the results, necessary adjustments to the model were done for use in mobilecommunications system designto improve Quality of Service (QoS) of networks. The proposed Simulation method employed in

this research proved to be more efficient, faster and accurate than the other two existing methods (i.e., Physical and Empirical

methods) that are mostly used. Also, the average propagation path loss in this work is lower than that of Okumura-Hata’s

average propagation path loss for urban and suburban areas. This implies that the method proposed in this our present work

leads to improvement in Qos of the investigated regions.

Index Terms:Radio Propagation model, Okumura-Hata model,Simulation method,Path loss andQoS.

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1 INTRODUCTION

adio propagation path loss model is an importanttool that characterizes the quality of mobile commu-nication because it determines effective radio cover-

age, as well as network optimization. The path loss mod-els predict, to a high level of accuracy, the true signalstrength reliability of the network and the quality of cov-erage [1]. With appropriate propagation path loss model,the coverage area of a mobile communication system, theSignal-to-Noise Ratio (SNR) and as well as the Carrier-to-Interference Ratio (C/I)can be easily determined.In Nige-ria where this study was carried out, the advent of mod-ern radio mobile communication such as the Global Sys-tem for Mobile communication (GSM) began its commer-cial operation in 2001. This makes this study new to ourenvironment and will be of interest to the Nigerian com-munity.The vision for the introduction of this Mobiletechnology by the Nigerian government was to expandthe country‘s tele-density, which was as low as about450,000 land lines for a population of well over 120 mil-lion people as at 2001 and also to make communicationcheap, available, reliable and accessible to the averageNigerians.

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IbikunleA. Frank is with Department of Electrical&Information Engi-neering, Covenant University, Ota, Nigeria

SomoyeAbiodun is with the Department of physics, Federal Polythecnic,Offa,Nigeria

Through the granting of licences to telecommunicationoperators by Nigerian Communications Commission(NCC) in Nigeria, the 450,000 connected subscriber linesin May 1999 has increased to96,616,580 lines across themobile networks operating in the country. This gives atele-density of 69.01as at February 2012 ending. August2011 marked the 10th anniversary of the introduction ofthe GSM in Nigeria. Before then, access to telephone wasthe exclusive preserve of the rich and privileged few inour society. Prior to the licensing of the Digital MobileOperators, private investment in the telecommunicationssector was just about $50 million. Between 2001 and now,the sector has attracted over $18billion, substantial part ofwhich is a direct foreign investment. This is apart fromthe billions of dollars that came to the government inform of revenues. Although, this mobile technology hastransformed the face of telecommunication in Nigeria, theconsumers‘ satisfaction in the country is not yet there. Alot of complaints such as poor quality of service, frequentcall drops, echo during radio conversation, poor inter-connectivity to and from other licensed networks, distor-tions and network congestions among other factors areathe disturbing issues that need to be solved [1].As at Jan-uary 2012, the United Kingdom, with 62 million popula-tion, has 66,000 BTSs (Base Transceiver Stations), while

Nigeria with 150 million people and over 90 million sub-scribers has 15,000 BTSs.The shortage of BTSs in Nigeriais reflected in the QoSprovided in the country. The vo-lume of successful telephone calls per minute made by

R



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Nigeria subscribers on all networks yearly decreasedfrom 45 billion to 41 billion as at December 2009 due topoor quality of services in the nation‘s telecoms sector.This manifested in forms of dropped calls, congestion onnetworks and service outage [2].It is however, a known fact that the quality of radio cov-erage of any wireless network design depends on the ac-curacy of the propagation model on which the networkwas built.It is of importance that the terrain of the city inwhich the network design will be deployed is taken intoconsideration so as to have high quality radio coverage.The model to be used must also be modified to suit theterrain.The usage and the accuracy of the model dependon the propagation environment [3].It is against thisbackground that a modified radio propagation path lossmodel is simulated using Visual Basic 6 in this work tosolve the problems of wireless network design in Ogunstate of Nigeria.The rest of this paper is organized as follows. In Section

II, we present overview f radio propagation path loss andpropagation models. In Section III, we discuss the varioussteps taken and methods used to achieved this field work.In Section IV, we present some simulation results anddiscuss the effectiveness of our proposed Simulation me-thod for obtaining radio propagation path loss in mobilewireless communication environment. Finally, Section Vcontains some concluding remarks.

2 OVERVIEW OF RADIO PROPAGATION PATH LOSS

2.1 Reasonsfor radio propagation path lossThe reasons for radio propagation path loss are discussedas follows: (i) Free space loss: The free space loss occurs as the signaltravels through space without any other effects attenuat-ing the signal; it will still diminish as it spreads out. Thiscan be thought of as the radio communications signalspreading out as an ever increasing sphere. As the signalhas to cover a wider area, the energy in any given areawill reduce as the area covered becomes larger.(ii) Absorption losses: Absorption losses occur if the radiosignal passes into a medium which is not totally transpa-rent to radio signals. It is likened to a light signal passingthrough transparent glass.(c)Diffraction losses:it occurs when an object appears inthe path. The signal can diffract around the object, butlosses do occur. The loss is higher for a more roundedobject. Radio signals tend to diffract better around sharpedges.(iii) Multipath: In a real terrestrial environment, signalswill be reflected and they will reach the receiver via anumber of different paths. These signals may add or sub-tract from each other depending upon the relative phasesof the signals. If the receiver is moved, the scenario willchange and the overall received signal will be found to

vary with position. Mobile receivers will be subject to thiseffect termed ―Rayleigh fading‖.(iv)Terrain: The terrain over which signals travel willhave a significant effect on the signal. Obviously

hills,which obstruct the path will considerably attenuatethe signal, often making reception impossible. Addition-ally, at low frequencies the composition of the earth willhave a marked effect. Dry sandy terrain gives higher le-vels of attenuation.(v)Buildings and vegetation: Buildings and other obstruc-tions including vegetation have a marked effect. Not onlywill buildings reflect radio signals, they will also absorbthem. Cellular communications are often significantlyimpaired within buildings. Trees and foliage can atte-nuate radio signals, particularly when wet.(vi)Atmosphere: The atmosphere can affect radio signalpaths. At lower frequencies, especially below 30 - 50MHz,the ionosphere has a significant effect by reflecting (ormore correctly refracting) them back to Earth. At frequen-cies above 50 MHz or more, the troposphere has a majoreffect, refracting the signals back to earth as a result ofchanging refractive index. For UHF broadcast, this canextend coverage to approximately a third beyond the ho-

rizon. The above discussedreasons represent some of themajor elements causing signal path loss for any radio sys-tem.

2.2 Radio Propagation Models

Propagation models are used extensively in networkplanning, particularly for conducting feasibility studiesand during initial deployment [4]. There are variouspropagation prediction models for mobile radio commu-nications systems. This includesLong-Rice model, Oku-mura-Hata‘s model, Lee‘s model, Durin‘s model, Wa l-

fisch and Bertoni‘s model, cost-123 model, ECC 33 modeletc. In this work, particular attention is given to the pre-diction model by Okumura-Hata. This is because themodel has been widely accepted and it will be used toevaluate the simulated results.The Okumura-Hata‘s model was first described by Yo-shihisa Okumura and then modified by Hata. The modelis based on the propagation measurements conducted inKanto, Tokyo, Japan. The initial model by Okumurapresents signal strength prediction curves over distancein a quasi-smooth urban area. From these predictioncurves,Hata developed a mathematical formulation forsimple computational applications.Hata presented theurban area propagation loss as a standard formula, alongwith additional correction factors for application in othersituations such as suburban, rural, among oth-ers.Therefore, this model is knownas Okumura-Hata‘smodel.However, the model neglects terrain profile be-tween transmitter and receiver i.e., hills and other ob-stacles between transmitter and receiver were not consi-dered.This is because both Hata and Okumura made theassumption that transmitter would normally be locatedon hills [3]. In using this model, radio transmission para-meters such as frequency, base station antenna height,mobile station antenna height, distance between mobile

and base station, terrain etc, must be taken into considera-tion. The Okumura-Hata model is mathematically ex-pressed as



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LP = x + y log10(fc) –13.82log10(ht)– a(hr) + (44.9 –

6.55log10 (ht)) log10 d............................................(1)

wherex and y are frequency dependent parame-

ters and they are given as 69.55 and 26.16 respec-

tively within the frequency range.

ht=transmitter antenna height (in meters),hr=receiver antenna height (in meters),

fc = the carrier frequency,

d = distance between transmitter and receiver,

anda(hr) =receiver correction factor, given as

a(hr)= (1.1log10(fc)–0.7)hr – (1.56log10(fc)–0.8) …….(2)

3 RESEARCH METHODOLOGY

3.1 Wave Propagation Methods

The following are the different methods used to deter-mine the radio wave propagation path loss in any wire-less communication environment.i) The Physical method:This method is used mostly forfeasibility studies. A transmitter is placed on the highestbuilding in the area and made to transmit at a given fre-quency i.e., 900MHz. A radio frequency analyzer is syn-chronized to the transmitter frequency and when this isdone, the RF analyzer distance will be varied and the re-sults for various distances are recorded. This will now becomputed using the Okumura-Hata equation and theresults compared with the Okumura-Hata‘s model resultii) The Measurement or Empirical method: This in-volved field measurement using an existing base stationand the distance from the station is varied say like 500meters. The receiving signal is used to calculate the pathloss. This method involves a site verification exercise fordata collection using a test kit consisting of the EricssonTEMS investigation software, mobile station with TEMSsoftware, GPS with 0db loss antenna, a Laptop with theTEMS software and Radio workssoftware. The equipmentsetup is placed inside a vehicle maintained at an averagespeed of about 30km/h. Data collection is done startingfrom a distance of 500m from the base station. The vehiclethen moves along the direction of the main lobe of the

directional antenna away from the site until it gets to thecoverage border. This process is repeated for the site un-der which the experiment is performed.iii) The Simulation or Theoretical method: This is doneby physical site survey ofthe selected sites. During thesites survey, any obstacles such as buildings, structures ortrees capable of causing obstruction of radio signal alongthe line of sight are identified. The average height of thebuilding, the vegetation of the site and any physical struc-ture like mountain, hills, valley etcare noted.This methodunlike the other two methods s used for both feasibilitystudies and initial deployment. For these reasons and

many more,this research work employed the Simula-tion/Theoretical method using Visual Basic software forthe simulation.

3.2 Investigating Sites Used

Ogunstate which is the investigating environment anditwas divided into three zones.From each zone, a site waspicked for the investigation. The first zone picked wasIlishan-Remo, the second was Abeokuta and the thirdwasSango-Ota. The Hata‘s model was modified to make it

suitable and applicable to the Nigeria terrain. The siteswere physically visited. During the site survey, any ob-stacles such as building and structures or trees capable ofcausing obstruction of radio signals were identified. Forthe model, ilishan was taken as a town, Sango-Ota as abig city, and Abeokuta was taken as a medium city.Ilishan-Remo town was chosen for this research work as atownwith terrain features such as dense vegetation most-ly tall trees, few palm trees, student hostels and tall resi-dential buildings. It lies within Latitude 6°54'0''North andLongitude 3°43'.0''East.The measurements were taken andas a town with a university campus, it is expected that thebuilding will be of two or more stories, which is calcu-

lated to be between 40meters-45meters. The expectedheight for a mast in a town is 30-35 meters, but a height of55 meters will be simulated for ilishan, having in mindthat the university may build high-rise buildings in thefuture.Sango-Otta with Latitude 709‘39‖North and Longitude3020‘54‖ was taken as a Mega-city because it is a twin cityand it has a direct link to Lagos. As a mega city we ex-pected it to have high-rise buildings and to be highly po-pulated.The terrain is flat surface with trees having anaverage height of 55meters, with big canopies that cancause attenuation of the signals. Also buildings around

the sites have an average height of 50 meters.The highpopulation,the tall trees and the building were all put intoconsideration when planning for this area. The simulatedheight for the sites is 70 meters, so as to cut off the attenu-ation in the siteAbeokuta with latitude 7o7‘03‖ North and Longitude3o18‘23‖ was taken as a city. The terrain is flat with build-ings of average height of 50 meters. The rock, which is 137meters above sea level, changes the terrain of the sites.The simulation height for the sites is 110 meters, as thiswill allow for a line of sight of antennas in the site. Thischosen height is not common for cities but the presence of

Olumo rock here was considered because it could causeattenuation.The Hata-Okumura equation given in equation (1) abovewas modified using the theoretical /simulated method asexplained earlier in section 2.2 above. The heights of theantennas at the investigating regions were predicted afterwhat could cause attenuation like the hills, buildings,trees etc have been taken into consideration. The distancewas so varied. This was transformed into computer appli-cation programming language based on the modified eq-uation given in equations (3), (4) and (5) below. The datagenerated from the simulated model were analyzed andcompared to those of Hata-Okumura model results.

3.3 Data Collection Procedures

The following steps were adopted in the collection of datafor the work. The cities, where measurement will be taken



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were identified. Survey of the cities to know the height ofthe buildings and vegetations that could affect path losswas done. We then identify the parameters to be used(Those to be constant and those to be varied). Modifica-tion of the Okumura-Hata‘s model was carried out to suitthe cities. The height and distance variables were estab-lished into the equations. These equations were simulatedusing Visual Basic 6 software. The simulated results ob-tained were analyzed and compared with Okumura-Hatamodel results for suburban and urban areas.

3.4 Okumura-Hata’s Model Equations

In carrying out this work, Hata‘s equations for predictingpropagation path loss (Lp) for different areas are restatedbelow.The Okumura-Hata‘s model equations forurban areas (i.e.Medium or Mega cities)are expressed in equation 3 andthat ofSuburban areas i.e. towns is expressed in equation6. a(hr),the correction factor for the receiving antenna in a

medium size city is given in equation 4.Thecorrectionfactor a(hr) for the receiving antenna for mega city is ex-pressed in equation 5, while a(hr) for Suburban areas isthe same with that of a medium city and it isexpressed asin equation 4.

a) For Urban areas (i.e. Medium or Mega cities). Lp (indBm) is the Okumura-Hata‘s equations for predictingpropagation path loss and it is expressed as

Lp = 69.55+26.16log10(fc) –13.82log10 h t –a(hr) +

(44.9–6.55 log10( ht)log10 d -------------------(3)

where, 150 ≤ fc ≤ 1500 (fc in MHz),

fc = Carrier frequency

30 ≤ h t ≤ 200 (h t in m) ,

ht= Transmitting antenna height,

1 ≤ d ≤ 20 (d in km),

d = distance between transmitter and receiver,

hr = Mobile station antenna,

with 1 ≤ hr ≤ 10 (hr in m)

a (hr) = the correction factor for the receiving an-

tenna is expressed as

For a Medium-size city:a(hr) = (1.1 log10 fc - 0.7) hr -

(1.56log10 fc – 0.8)………………......................... (4)

For Mega –size city:a(hr) = 8.29(log10 1.54hr)² - 1.1,

when ( fc ≤ 200MHz)

or a(hr) = 3.2 (log10 11.75hr)² - 4.97,

when(fc ≥ 400MHz)...........................................(5)

(b) For Suburban areas i.e. Towns

Lps = Lp (Urban)- 2 [log10 (28

c f )]² - 5.4..................... (6)

3.5 The Modified Hata’s Equations The modifications were done based on the theoreticalmethod mentioned in section 3.1.3. Okumura-Hata‘smodel neglects terrain profile between transmitter andreceiver i.e., hills and other obstacles between transmitterand receiver were not considered [3]. This is because bothHata and Okumura made the assumption that transmitterwould normally be located on hills. The assumptions arecorrected by taking the terrains of the investigated sitesinto consideration and the heights were predicted basedon the terrains.

For Mega-size city (Sango-Ota)

Lp = 69.55+26.16log10(900MHz) –13.82log10 (70m)

–a(hr) + (44.9–6.55 log10(70m)log10 d ---(7)

where

150 ≤ fc ≤ 1500 (fc in MHz); fc = carrier frequency30 ≤ ht ≤ 200 (ht is in meters);

ht = transmitting antenna height

1 ≤ d ≤ 20 (d in km);

d = Distance between transmitter and receiver

a (hr) = the correction factor for the receiving an-

tenna, expressed as

a(hr) = 3.2 (log10 11.75hr)² - 4.97;

fc ≥ 400MHz (For Mega-size city)

For a Medium-size city (Abeokuta)

Lp = 69.55+26.16log10(900) –13.82log10 (110m) –

a(hr) + (44.9–6.55 log10(110m)log10 d...........(8)

a(hr) = (1.1 log10 fc - 0.7) hr - (1.56log10 fc – 0.8)

(For a medium-size city)

where 1 ≤ hr ≤ 10. (hr is in meters)

For Suburban areas (i.e., Ilishan as town)

Lp = 69.55 + 26.16log10 (fc) – 13.82 log10 (55m) –

a(hr) + (44.9 – 6.55log10(55m)log10d) - 2[log10

(28

c f )]² - 5.4.............................................................. (9)

a(hr) = (1.1 log10 fc - 0.7) hr - (1.56log10 fc – 0.8,

(same for medium city)

4 SIMULATION RESULT AND DISCUSSIONS

The modified equations in (7), (8) and (9) were simulatedwith Visual Basic 6 programming language. The desktop

view of the simulation is as shown in Figure 1. The firstoption in the desktop is to Select the City Box that hasSuburban area, Mega city and Medium city. The Secondoption in the Select Distance has distance from 0.5km to



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10.0km as options.The third option shows the Propagation result indBm.The Compute box is used to run the program andthe Exit box to exit the program.To get the propagationresult, one option from the first option box is selected; thedistance will be selected from the second option box andthe compute box is selected also. The result of propaga-tion loss in dBm is computed and displayed.The programcan be terminated by selecting the Exit option box.

Fig.1. Desktop view of the Modified Okumura-Hata‘sequation for propagation path loss

The simulated results for Sango-Ota (Mega-city), Abeoku-ta (as City) and Ilishan-Remo (as Suburban) are shown inTable 1. Table 2 shows the comparison of the simulatedpath loss results with the Okumura-Hata‘s model resultsfor urban area. Table 3 shows the comparison of the simu-lated path loss results with Okumura-Hata‘s model re-

sultsfor suburban.

TABLE 1SIMULATED PATH LOSS RESULTS IN DBMFOR SANGO-OTA, AB-

EOKUTAAND IN ILISHAN

Tx/Rx Dis-tances

Sango-Ota Abeokuta Ilishan-Remo

0.5KM 108.18 109.11 103.33

1.0KM 118.06 118.61 112.82

1.5KM 123.84 124.16 118.37

2.0KM 127.94 128.10 122.31

2.5KM 131.12 131.15 125.37

3.0KM 133.72 133.65 127.873.5KM 135.91 135.76 129.98

4.0KM 137.82 137.59 131.8

4.5KM 139.49 139.2 133.42

5.0KM 141.0 140.64 134.86

5.5KM 142.35 141.95 136.17

6.0KM 143.59 143.14 137.36

6.5KM 144.74 144.24 138.45

7.0KM 145.79 145.25 139.47

7.5KM 146.77 146.19 140.41

8.0KM 147.69 147.08 141.30

8.5KM 148.56 147.91 142.13

9.0KM 149.37 148.69 142.91

9.5KM 150.14 149.43 143.6510.0KM 150.87 150.13 144.35

Figure 2 shows the graphical representation of the mod-ified simulated results. Figure 3 shows graphical repre-

sentation of the comparison of the urban simulated pathloss results with the Okumura-Hata‘s model results forurban area. Figure 4 shows the graphical representationof the comparison of suburban simulated path loss resultwith Okumura-Hata‘s model for suburban. All the graph-ical representations were done using Microsoft Excel.

Fig.2. Graphical representations of simulated results forthe three areas

The results from Table 1 show that as distance increasesthe path loss also increases. This implies that as onemoves away from the mast, the signal level from trans-mitter to the receiver also drops.The graph in Figure 2 shows that as distance increases,the path loss increases. Sango-Ota has the highest pathloss, followed by Abeokuta while the least is Ilishan-Rem.

TABLE 2COMPARISON OF SANGO-OTA AND ABEOKUTA SIMULATED PATH

LOSS RESULTS IN DBMWITH OKUMURA-HAHA’S MODEL

Tx/Rx Dis-tances

Sango-Ota

Abeo-kuta

Okumura-Hata’s Model

0.5KM 108.18 109.11 123.66

1.0KM 118.06 118.61 134.26

1.5KM 123.84 124.16 140.46

2.0KM 127.94 128.10 144.86

2.5KM 131.12 131.15 148.28

3.0KM 133.72 133.65 151.06

3.5KM 135.91 135.76 153.42

4.0KM 137.82 137.59 155.47

4.5KM 139.49 139.2 157.27

5.0KM 141.0 140.64 158.88

5.5KM 142.35 141.95 160.34

6.0KM 143.59 143.14 161.60

6.5KM 144.74 144.24 162.89

7.0KM 145.79 145.25 164.04

7.5KM 146.77 146.19 165.08

8.0KM 147.69 147.08 166.07

8.5KM 148.56 147.91 166.99

9.0KM 149.37 148.69 167.87

9.5KM 150.14 149.43 168.7010.0KM 150.87 150.13 169.48

In Table 2, the urban simulated path loss results were

The Simulated result graph

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Distance (m)

P a t h l o s s ( d B m )

SANGO-OTA

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ILISHAN-REMO



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compared with that of Okumura-Hata‘s model for urban area.It shows that the simulated results have low pathloss compared with that of Okumura-Hata‘s model forthe urban, The average path loss for Sango-Ota is138.35dBm, while the average path loss for Abeokuta is138.10 dBm, the average path loss for the Okumura-Hata‘s model for urban area is 156.03dBm.This impliesthat the simulation method has considered the terrainproblem of the urban area in Nigeria and can be used tosolve the path loss caused byterrain.

The graph in Figure 3: shows that for the same distances,the simulated path losses for urban areas are lower thanthat of Hata‘s for suburban area. This implies that thesimulated method can be used to reduce path loss thataffects the QoS taken the terrain of the urban areas intoconsideration. It is evidence here thatthe differences inour modified simulated path loss results and the Okumu-ra-Hata‘s path loss results is certainly due to the diffe r-

ences in the topology and in the terrains between thecho-sen areas in Ogun state, Nigeria and Tokyo in Japan.

In Table 3, the Suburban simulated path loss result wascompared with that of Okumura-Hata‘s model for Subur-ban. It shows that the simulated results have low pathloss compared with that of Okumura-Hata‘s model forSuburban.

TABLE 3COMPARISON OF ILISHAN (SUBURBAN) SIMULATED PATH LOSS

RESULTS IN DBMWITH OKUMURA-HATA’S

Tx/Rx Dis-

tances

Ilishan-Remo Okumura-Hata

Model(Suburban0.5KM 103.33 122.52

1.0KM 112.82 133.12

1.5KM 118.37 139.32

2.0KM 122.31 143.72

2.5KM 125.37 147.14

3.0KM 127.87 149.92

3.5KM 129.98 152.28

4.0KM 131.80 154.33

4.5KM 133.42 156.13

5.0KM 134.86 157.74

5.5KM 136.17 159.20

6.0KM 137.36 160.53

6.5KM 138.45 161.75

7.0KM 129.47 162.90

7.5KM 140.41 164.66

8.0KM 141.30 164.93

8.5KM 142.13 165.85

9.0KM 142.91 166.73

9.5KM 143.65 167.56

10.0KM 144.35 168.34

The average path loss for Ilishan-Remo is 132.32dBm

while the average path loss for Okumura-Hata‘s subur-ban area is 154.93dBm.This implies also that the simu-lated method has considered the terrain problem of theSuburban area in Nigeria and the methodcan be used to

solve the path loss caused by the terrain.

Fig.3. Graphical representations of the Path loss for theurban areas

Fig 4Graphical representations of the Path loss for suburban areas

The graph in Figure 4 shows that, for the same distances,the simulated path loss for the suburban area is lowercompared to that of Hata for suburban area. This impliesthat the simulated method can be used to reduce path lossthat affects the QoS, taking into consideration the terrainof the suburban area. The differences in the simulatedpath loss results and the Okumura-Hata‘s path loss re-sults is certainly due to the differences in the topologyand the differences in the terrain of Ilishan-Remo in Ogun

State, Nigeria and Tokyo.

5 CONCLUSION

Radio transmission in a mobile communication systemoften takes place over irregular terrains. The terrain pro-file of a particular area needs to be taken into account inestimating the path loss. The terrain profile may varyfrom a simple curvature of the earth surface profile to ahighly curved mountainous profile. From this research, itwas established that all radio systems suffer path loss,which is influenced by either the distance between trans-

mitter or the receiver, motions and terrains. The resultsfrom this research show that path loss in Ogun State canbe reduced if different terrains are taken into considera-tion during network planning, particularly when con-ducting feasibility studies, initial deployment and during

Graph result for urban area

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p a t h l o s s ( d B m ) SANGO-OTA

ABEOKUTA

Hata's Path loss for

Urban Area

Graph result for suburban area

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(Suburban Area)



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deployment. This work developed a model that can helpin planning better mobile wireless network and in ad-dressing the complaints of poor quality of mobile net-work services in Ilishan-Remo, Sango-Otta, and Abeokutain Ogun State, Nigeria.The simulated results from the modified Hata-Okumuramodel will help in the planning and in the optimizationof the networks of the investigated environment. Sincethe proposed simulated method as shown in our work isbetter and fasterin obtaining good propagation path lossthan the Empirical method, the method can be used dur-ing network planning, for conducting feasibility studiesand during initial deployment in any part of Nigeria. Thiswill save time and cost used to build the antenna towers,and will improve the Quality of Services of mobile net-works in Ogun State and Nigeria in particular.The majorreasons for poor QoS is bad planning during initial de-velopment, all these can be solved by using our proposedsimulated model presented in this paper.

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Ibikunle Frank Ayo (JP), received the B.Tech. degree inElec-trical Engineering from University of Science and Technology,

Port-Harcourt, Nigeria in 1986. In 1993, he won himself a Fed-eral Government Scholarship award to study abroad and ob-tained a Doctorate degree in Telecommunications and Informa-tion Engineering (by research work) in 1997 from the Universityof Posts and Telecommunications, Beijing, China. He has 26years practical working experience with the largest telecommu-nications carrier company in Nigeria (called NITEL) and the

mobile arm of the NITEL Ltd. (called MTEL) before joiningthe academics. He is presently at Covenant University, Electri-cal and Information Department of the School of Engineering,College of Science and Technology, Ota, Nigeria. His presentareas of research are in Next Generation Converged-IP Net-

work, Artificial Neural Networks for Signal Processing, CloudComputing and Broadband Wireless/Wired Access Technolo-gies (OFDM, MIMO, WiMAX, WiFi, 3G/4G, FTTx). He hasover 30 technical publications in both national and international journals and several conference proceedings. He is a member of the following professional societies: Council for the Regulationof Engineering in Nigeria (COREN), Nigerian Society of Engi-neers (NSE), MIEEE and Nigerian Institute of Management(NIM). Dr Ayoleke Ibikunle is happily and fruitfully married.

Somoye Abiodunholds BSc and MSc. degrees from Universityof Lagos and Babcock University IIishan, Nigeria in Computer Science in 1991 and 2012 respectifully. His research interest is inNetwork and Telecommunications Systems.

Documents

Radio Wave Propagation Path Loss Modelling in Mobile Communications Environment: a Case Study of Ogun-state in Nigeria