Path loss Prediction Models for Wireless … loss Prediction Models for Wireless Communication Channels and its Comparative Analysis 39 C. Stochastic models Stochastic models, the

International Journal of Engineering, Management & Sciences (IJEMS)ISSN-2348 –3733, Volume-2, Issue-3, March 2015

38 www.alliedjournals.com

Abstract— The aim of this paper is investigate the

performance of various path loss models in differentenvironments for determination of the signal strength withrespect to various frequency ranges and distance for wirelessnetwork. There are five path loss models, namely Free Space,Log-distance, Log-normal, Okumura/Hata, IEEE802.16dmodels. These models have been reviewed with differentreceiver antenna heights in urban, suburban and ruralenvironments. Free space path loss model is used as referencevalue for produced the estimated results value. Then compare allestimated results of reviewed models with the reference modelvalues. Hata model demonstrated good performance in terms ofreceived signal strength or indirectly, reduction in path-loss.Log-normal model based on the shadowing effect whilecalculating the values of path-loss. IEEE802.16d model andlog-normal model are similar. And it is a standard model usedfor the measurements of path-loss in sub-urban area. However,Hata model could be preferred due to better performance interms of less path loss as compared with the results of referencemodel at lower receiver antenna heights for urban and openarea environments.

Index Terms— Path loss, Log-distance, Log-normal,Okumura/Hata.

I. INTRODUCTION

The losses occurred in between transmitter and receiver isknown as propagation path loss In wireless communication.Path loss is the unwanted reduction in power single whichis transmitted. This path loss in different area like rural,urban, and suburban with the help of propagation pathloss models are measured by author.

The wireless channel environment is mainly govern theperformance of wireless communication systems. Channelenvironment plays main role in wireless communication. Sothe foundation for the development of high performance andbandwidth-efficient wireless transmission technology is veryimportant.

In other words, the propagation of a radio wave is acomplicated and less predictable process. There are threephenomenon that affects reflection, diffraction, andscattering, whose intensity varies with different environment sat different instances.

Manuscript received March 22, 2015Santosh Choudhary, M. Tech. Scholar, Rajasthan College of Engineering

for Women, JaipurDinesh Kumar Dhaka, Asst. Professor, Rajasthan College of Engineeringfor Women, Jaipur

Fading: A unique characteristic in a wireless channel is aphenomenon called ‘fading,’ the variation of the signalamplitude over time and frequency. Mainly two reason takeinto account for fading, multipath propagation, referred to asmulti-path (induced) fading, or to shadowing from obstaclesthat affect the propagation of a radio wave, referred to asshadow fading.

Fig. 1 Path Loss, Shadowing and Multipath versusDistance

Advantage of predicting model: By performing simulationscalculated by propagation models over the area ofdemand, the change in the network coverage can bepredicted. The more accurate the prediction model, easier itgets to develop the cellular network.

II. VARIOUS PATH-LOSS MODELSThese models can be broadly categorized into three types;empirical, deterministic and stochastic.A. Empirical modelsEmpirical models are those based on observations andmeasurements alone. These models are mainly used to predictthe path loss, rain-fade and multipath have also beenproposed. Empirical models can be split into twosubcategories namely, time dispersive and non-timedispersive.B. Deterministic modelsThe deterministic models make use of the laws governingelectromagnetic wave propagation to determine thereceived signal power at a particular location.Deterministic models often require a complete 3-D map of thepropagation environment. An example of a deterministicmodel is a ray tracing model.

Path loss Prediction Models for WirelessCommunication Channels and its Comparative

AnalysisSantosh Choudhary, Dinesh Kumar Dhaka

Path loss Prediction Models for Wireless Communication Channels and its Comparative Analysis


C. Stochastic modelsStochastic models, the environment as a series of random

variables. These models are the least accurate but require theleast information about the environment and use much lessprocessing power to generate predictions.

III. SIMULATION AND EXPERIMENTAL RESULTS

A. General Path-loss Model

The free-space propagation model is used for predicting thereceived signal strength in the line-of-sight (LOS)environment where there is no obstacle between thetransmitter and receiver. The power radiated by an isotropicantenna is spread uniformly and without loss over the surfaceof a sphere surrounding the antenna. It is often use for thesatellite communication.Letd- distance in meters between the transmitter and receiver.Gt transmit gain andGr receive gain,Pr(d)the received power at distance d,

Friis equation [1], given as

....................................(1)

where Pt represents the transmit power (watts),λ is the wavelength of radiation (m), and

L is the system loss factor which is independent ofpropagation environment .In general, L > 1, but L=1 if we assume that there is no loss inthe system hardware. It is obvious from Equation (1.1) thatthe received power attenuate s exponentially with the distanced. The free-space path loss, PLF(d), without any system losscan be directly derived from Equation (1) with L=1as

............................(2)

……………(3)

Without antenna gains (i.e., Gt = Gr = 1), Equation (2) isreduced to

..........................(4)

Figure 2 shows the free-space path loss at the carrierfrequency of fc = 1.5 GHz for different antenna gains as thedistance varies.

Figure 2 Free Space Path-Loss model

Here clear that the path loss increases by reducing the antennagains. The average received signal in all the other actualenvironments decreases with the distance between thetransmitter and receiver, d, in a logarithmic manner.B. Log-distance path loss modelIn fact, a more generalized form of the path loss model can be

constructed by modifying the free-space path loss with thepath loss exponent n that varies with the environments. This isknown as the log-distance path loss model, in which the pathloss at distance d is given as

....(5)

where d0 is a reference distance. As shown in Table 1, the pathloss exponent can vary from 2 to 6, depending on thepropagation environment. However, it could be 100 m or 1 m,respectively, for a macro-cellular system with a cell radius of1km or a micro cellular system with an extremely small radius[5].

TABLE-1Path-loss exponent

Environment Path-lossExponent (n)

Free space 2

Urban Area cellular radio 2.7-3.5

Shadowed urban cellularradio

3-5

In-building line-of-sight 1.6-1.8

Obstructed in building 4-6

Obstructed in factories 2-3

the carrier frequency of fc=1.5 GHz.



Figure 3 Log-distance Path-Loss model

It is clear that the path loss increases with the path lossexponent n.C. Log- normal path loss ModelIf the distance between the transmitter and receiver is equal toeach other, every path may have different path loss since thesurrounding environments may vary with the location of thereceiver in practice. A log- normal shadowing model is usefulwhen dealing with a more realistic situation. Let Xσ denote aGaussian random variable with a zero mean and a standarddeviation of s. Then, the log- normal shadowing model isgiven as

.......................................(6)The log-normal shadowing model at fc=1.5 GHz with σ=3 dBand n=2. It clearly illustrates the random effect of shadowingthat is imposed on the deterministic nature of the log-distancepath loss model.

Figure 4 Log-normal shadowing Path-Loss model

IV. OKUMURA/HATA MODELThe Okumura model has been obtained through extensiveexperiments to compute the antenna height and coverage areafor mobile communication systems. The Okumura model was

built into 3 modes. These are urban, suburban and open areas.The major disadvantage with the model is its slow response torapid changes in terrain region, therefore the model is fairlygood in urban and suburban areas, but not as good in ruralareas. However, The Hata model is one of the most frequentlyadopted path loss models that can predict path loss in an urbanarea. This particular model mainly covers the typical mobilecommunication system characteristics with a frequency bandof 500–1500 MHz, cell radius of 1–100 km, and an antennaheight of 30m to 1000m. The path loss at distance d in theOkumura model is given as

...........................................(7)where AMU(f,d) -medium attenuation factor at frequency f,GRx - the antenna gains of Rx andGTx- the antenna gains of Tx, and

GAREA is the gain for the propagation environment in thespecific area.Note that the antenna gains, GRx and GTx, are merely a

function of the antenna height, without other factors taken intoaccount like an antenna pattern. Meanwhile, AMU(f,d) andGAREA can be referred to by the graphs that have beenobtained empirically from actual measurements by Okumura.The Okumura model has been extended to cover the variouspropagation environments, including urban, suburban, andopen area, which is now known as the Hata model. In fact, theHata model is currently the most popular path loss model. Forthe height of transmit antenna, hTX [m], and the carrierfrequency of fc [MHz], the path loss at distance d [m] in anurban area is given by the Hata model as

...........................................(8)where CRX is the correlation coefficient of the receiveantenna, which depends on the size of coverage. For small tomedium-sized coverage, CRX is given as

.......................................................(9)where hRX[m] is the height of transmit antenna. Forlarge-sized coverage, CRX depends on the range of the carrierfrequency, for example,

.......................................................(10)Meanwhile, the path loss at distance d in suburban and openareas are respectively given by the Hata model as

.......................................(11)



and

...........(12)Figure5 presents the path loss graphs for the three differentenvironments – urban, suburban, and open areas . It is clearthat the urban area gives the most significant path loss.

Figure 5 Hata Path-Loss model

V. IEEE 802.16D MODELIEEE 802.16d model is based on the log-norm al shadowingpath loss model. There are three different types of models(Type A, B, and C), depending on the density of obstructionbetween the transmitter and receiver (in terms of treedensities) in a macro-cell suburban area. Referring to [8–11],the IEEE 802.16d path loss model is given as

………………………….(13)

In Equation (12), d0=100m and γ = a - bhTX + c/hTX where a, b,and c are const ants that vary with the types of channel modelsas given in Table 3, and h TX is the height of transmit antenna(typically, range d from 10 m to 80 m).

TABLE-2Types of IEEE 802.16d path loss models

Type Description

A Macro-cell suburban, ART to BRT for hillyterrain with moderate-to-heavy tree densities

B Macro-cell suburban, ART to BRT forintermediate path loss condition

C Macro-cell suburban, ART to BRT for flatterrain with light tree densities

Furthermore, Cf is the correlation coefficient for the carrierfrequency fc [MHz], which is given as

............14TABLE-3

Parameters for IEEE 802.16d type A, B, and C modelsParameter Type A Type B Type C

A 4.6 4 3.6

B 0.0075 0.0065 0.005

C 12.6 17.1 20

Mean while, CRX is the correlation coefficient for the receiveantenna, given as

.....

1.15or

.......16

The correlation coefficient in Equation (1.15) is based on themeasurements by AT&T while the one in Equation (16) isbased on the measurements by Okumura.Figure 6 shows the path loss by the IEEE 802.16d model atthe carrier frequency of 2 GHz, as the height of the transmitantenna is varied and the height of the transmit antenna isfixed at 30m.

Figure 6 IEEE 802.16d Path-Loss model

Note that when the height of the transmit antenna is changedfrom 2m to 10m, there is a discontinuity at the distance of100m, causing some inconsistency in the predict ion of thepath loss. It implies that a new reference distance d0` must bedefined to modify the existing model [9]. The new referencedistance d0` is determined by equating the path loss inEquation (12) to the free-space loss in Equation (3), such that



...............(17)Solving Equation (17) for d0`, the new reference distance isfound as

........................(18)Substituting Equation (1.18) into Equation (1.12), a modifiedIEEE 802.16d model follows as

……………………………19

Figure7 shows the path loss by the modified IEEE 802.16dmodel

Fig. 7 Modified IEEE 802.16d Path-Loss model

VI. CONCLUSIONIn this work three different propagation models were studiedin various frequency bands and were compared to each others.Each one of them have their advantages and disadvantagesbased on the calculations carried out and the environmentalconditions taken into account. In all, we can say that Hatamodel is the most used model out of all as this model takesintoconsideration various environmental conditions, including thevarious types of area.

1.The free-space propagation model is used for predicting thereceived signal strength in the line-of-sight (LOS)environment where there is no obstacle between thetransmitter and receiver.

2.The log-distance model is constructed by modifying thefree-space path loss with the path loss exponent n that varieswith the environments. The limitation with the above twomodels is that they do not take into account the condition that

every path may have different path loss since the surroundingenvironments may vary with the location of the receiver inpractice.

3.When dealing with a more realistic situation, log-normalshadowing model is useful i.e., it allows the receiver at thesame distance d to have a different path loss, which varieswith the random shadowing effect Xσ.

4.The most frequently adopted path loss models is The Okumuramodel, it can predict path loss in an urban area. Modifidedform is now known as the Hata model.

5.The IEEE802.16d model is based on the log-normal shadowingpath loss model considering the density of obstructionbetween the transmitter and receiver (in terms of treedensities) in a macro-cell suburban area.

ACKNOWLEDGEMENTThe work presented in this paper has been done in M. Tech aspart of Thesis at department of Electronics & communication,RCEW, Jaipur, RTU Kota.

REFERENCES

[1] Ekiz, E.; Sokullu, R., "Comparison of path loss prediction models andfield measurements for cellular networks in Turkey,", 2011 InternationalConference on Selected Topics in Mobile and Wireless Networking(iCOST), vol., no., pp.48,53, 10-12 Oct. 2011[2]Farhoud, M.; El-Keyi, A; Sultan, A, "Empirical correction of theOkumura-Hata model for the 900 MHz band in Egypt,", 2013 ThirdInternational Conference on Communications and Information Technology(ICCIT), vol., no., pp.386,390, 19-21 June 2013[3]Fischer, J.; Grossmann, M.; Felber, W.; Landmann, M.; Heuberger, A, "Ameasurement-based path loss model for wireless links in mobile ad-hocnetworks (MANET) operating in the VHF and UHF band,", 2012 IEEE-APSTopical Conference on Antennas and Propagation in WirelessCommunications (APWC), vol., no., pp.349,352, 2-7 Sept. 2012[4]Dotche, K. A; Diawuo, K.; Ofosu, W. K., "Effect of path loss model onreceived signal: Using Greater Accra, Ghana as case study," WirelessTelecommunications Symposium (WTS), 2012 , vol., no., pp.1,6, 18-20April 2012[5]Nisirat, M.A; Ismail, M.; Nissirat, L.; AlKhawaldeh, S.; Yuwono, T., "AHata based model utilizing terrain roughness correction formula,", 2011 6thInternational Conference on Telecommunication Systems, Services, andApplications (TSSA), vol., no., pp.284,287, 20-21 Oct. 2011[6]Ge, Yiqun; Shi, Wuxian; Sun, Guobin, "Impacts of Different SUIChannel Models on Iterative Joint Synchronization in Wireless-MANOFDM system of IEEE802.16d,", 2005 6th IEE International Conference on3G and Beyond, vol., no., pp.1,4, 7-9 Nov. 2005[7]Tan, S.Y.; Tan, M. Y.; Tan, H. S., "Multipath delay measurements andmodeling for interfloor wireless communications,", IEEE Transactions onVehicular Technology, vol.49, no.4, pp.1334,1341, Jul 2000[8] Yan Wu; Min Lin; Wassell, I, "Path loss estimation in 3D

environments using a modified 2D Finite-Difference Time-Domaintechnique,", 2008. CEM 2008. 2008 IET 7th International Conference onComputation in Electromagnetics, vol., no., pp.98,99, 7-10 April 2008[9]Maitham Al-Safwani and Asrar U.H. Sheikh, “Signal StrengthMeasurement at VHF in the Eastern Region of Saudi Arabia”, The ArabianJournal for Science and Engineering, Vol. 28, No.2C, pp.3 -18, December2003.[10] Rautiainen, T.; Wolfle, G.; Hoppe, R., "Verifying path loss and delayspread predictions of a 3D ray tracing propagation model in urbanenvironment," Proceedings. VTC 2002-Fall. 2002 IEEE 56th VehicularTechnology Conference, 2002., vol.4, no., pp.2470,2474 vol.4, 2002[11] Karedal, J.; Czink, N.; Paier, A; Tufvesson, F.; Molisch, AF., "PathLoss Modeling for Vehicle-to-Vehicle Communications," VehicularTechnology, IEEE Transactions on , vol.60, no.1, pp.323,328, Jan. 2011



[12] Erceg, V.; Greenstein, L.J.; Tjandra, S.Y.; Parkoff, S.R.; Gupta, A;Kulic, B.; Julius, AA; Bianchi, R., "An empirically based path loss model forwireless channels in suburban environments,", IEEE Journal on SelectedAreas in Communications, vol.17, no.7, pp.1205,1211, Jul 1999[13] Singh, Y. "Comparison of Okumura, Hata and COST-231 Models onthe Basis of Path Loss and Signal Strength." International Journal ofComputer Applications (0975-8887) 59.11 (2012).

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Path loss Prediction Models for Wireless … loss Prediction Models for Wireless Communication Channels and its Comparative Analysis 39 C. Stochastic models Stochastic models, the