International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 3733, Volume-2, Issue-3, March 2015 Path loss Prediction Models for Wireless Communication Channels and its Comparative Analysis Santosh Choudhary, Dinesh Kumar Dhaka Abstract The aim of this paper is investigate the performance of various path loss models in different environments for determination of the signal strength with respect to various frequency ranges and distance for wireless network. There are five path loss models, namely Free Space, Log-distance, Log-normal, Okumura/Hata, IEEE802.16d models. These models have been reviewed with different receiver antenna heights in urban, suburban and rural environments. Free space path loss model is used as reference value for produced the estimated results value. Then compare all estimated results of reviewed models with the reference model values. Hata model demonstrated good performance in terms of received signal strength or indirectly, reduction in path-loss. Log-normal model based on the shadowing effect while calculating the values of path-loss. IEEE802.16d model and log-normal model are similar. And it is a standard model used for the measurements of path-loss in sub-urban area. However, Hata model could be preferred due to better performance in terms of less path loss as compared with the results of reference model at lower receiver antenna heights for urban and open area environments. Index Terms Path loss, Log-distance, Log-normal, Okumura/Hata. I. INTRODUCTION The losses occurred in between transmitter and receiver is known as propagation path loss In wireless communication. Path loss is the unwanted reduction in power single which is transmitted. This path loss in different area like rural, urban, and suburban with the help of propagation path loss models are measured by author. The wireless channel environment is mainly govern the performance of wireless communication systems. Channel environment plays main role in wireless communication. So the foundation for the development of high performance and bandwidth-efficient wireless transmission technology is very important. In other words, the propagation of a radio wave is a complicated and less predictable process. There are three phenomenon that affects reflection, diffraction, and scattering, whose intensity varies with different environment s at different instances. Manuscript received March 22, 2015 Santosh Choudhary, M. Tech. Scholar, Rajasthan College of Engineering for Women, Jaipur Dinesh Kumar Dhaka, Asst. Professor, Rajasthan College of Engineering for Women, Jaipur 38 Fading: A unique characteristic in a wireless channel is a phenomenon called fading, the variation of the signal amplitude over time and frequency. Mainly two reason take into account for fading, multipath propagation, referred to as multi-path (induced) fading, or to shadowing from obstacles that affect the propagation of a radio wave, referred to as shadow fading. Fig. 1 Path Loss, Shadowing and Multipath versus Distance Advantage of predicting model: By performing simulations calculated by propagation models over the area of demand, the change in the network coverage can be predicted. The more accurate the prediction model, easier it gets to develop the cellular network. II. VARIOUS PATH-LOSS MODELS These models can be broadly categorized into three types; empirical, deterministic and stochastic. A. Empirical models Empirical models are those based on observations and measurements alone. These models are mainly used to predict the path loss, rain-fade and multipath have also been proposed. Empirical models can be split into two subcategories namely, time dispersive and non-time dispersive. B. Deterministic models The deterministic models make use of the laws governing electromagnetic wave propagation to determine the received signal power at a particular location. Deterministic models often require a complete 3-D map of the propagation environment. An example of a deterministic model is a ray tracing model.
Path loss Prediction Models for Wireless Communication Channels and its Comparative Analysis C. Stochastic models Stochastic models, the environment as a series of random variables. These models are the least accurate but require the least information about the environment and use much less processing power to generate predictions. III. SIMULATION AND EXPERIMENTAL RESULTS A. General Path-loss Model The free-space propagation model is used for predicting the received signal strength in the line-of-sight (LOS) environment where there is no obstacle between the transmitter and receiver. The power radiated by an isotropic antenna is spread uniformly and without loss over the surface of a sphere surrounding the antenna. It is often use for the satellite communication. Let d- distance in meters between the transmitter and receiver. Gt transmit gain and Gr 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 of propagation environment. In general, L > 1, but L=1 if we assume that there is no loss in the system hardware. It is obvious from Equation (1.1) that the received power attenuate s exponentially with the distance d. The free-space path loss, PLF(d), without any system loss can be directly derived from Equation (1) with L=1as...(2) (3) Without antenna gains (i.e., Gt = Gr = 1), Equation (2) is reduced to...(4) Figure 2 shows the free-space path loss at the carrier frequency of fc = 1.5 GHz for different antenna gains as the distance varies. Figure 2 Free Space Path-Loss model Here clear that the path loss increases by reducing the antenna gains. The average received signal in all the other actual environments decreases with the distance between the transmitter and receiver, d, in a logarithmic manner. B. Log-distance path loss model In fact, a more generalized form of the path loss model can be constructed by modifying the free-space path loss with the path loss exponent n that varies with the environments. This is known as the log-distance path loss model, in which the path loss at distance d is given as...(5) where d0 is a reference distance. As shown in Table 1, the path loss exponent can vary from 2 to 6, depending on the propagation environment. However, it could be 100 m or 1 m, respectively, for a macro-cellular system with a cell radius of 1km or a micro cellular system with an extremely small radius [5]. TABLE-1 Path-loss exponent Environment Path-loss Exponent (n) Free space 2 Urban Area cellular radio 2.7-3.5 Shadowed urban cellular radio In-building line-of-sight 3-5 1.6-1.8 Obstructed in building 4-6 Obstructed in factories 2-3 the carrier frequency of fc=1.5 GHz. 39
International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 3733, Volume-2, Issue-3, March 2015 built into 3 modes. These are urban, suburban and open areas. The major disadvantage with the model is its slow response to rapid changes in terrain region, therefore the model is fairly good in urban and suburban areas, but not as good in rural areas. However, The Hata model is one of the most frequently adopted path loss models that can predict path loss in an urban area. This particular model mainly covers the typical mobile communication system characteristics with a frequency band of 500 1500 MHz, cell radius of 1 100 km, and an antenna height of 30m to 1000m. The path loss at distance d in the Okumura model is given as Figure 3 Log-distance Path-Loss model It is clear that the path loss increases with the path loss exponent n. C. Log- normal path loss Model If the distance between the transmitter and receiver is equal to each other, every path may have different path loss since the surrounding environments may vary with the location of the receiver in practice. A log- normal shadowing model is useful when dealing with a more realistic situation. Let Xσ denote a Gaussian random variable with a zero mean and a standard deviation of s. Then, the log- normal shadowing model is given as...(6) The log-normal shadowing model at fc=1.5 GHz with σ=3 db and n=2. It clearly illustrates the random effect of shadowing that is imposed on the deterministic nature of the log-distance path loss model....(7) where AMU(f,d) -medium attenuation factor at frequency f, GRx - the antenna gains of Rx and GTx- the antenna gains of Tx, and GAREA is the gain for the propagation environment in the specific area. Note that the antenna gains, GRx and GTx, are merely a function of the antenna height, without other factors taken into account like an antenna pattern. Meanwhile, AMU(f,d) and GAREA can be referred to by the graphs that have been obtained empirically from actual measurements by Okumura. The Okumura model has been extended to cover the various propagation environments, including urban, suburban, and open area, which is now known as the Hata model. In fact, the Hata model is currently the most popular path loss model. For the height of transmit antenna, htx [m], and the carrier frequency of fc [MHz], the path loss at distance d [m] in an urban area is given by the Hata model as...(8) where CRX is the correlation coefficient of the receive antenna, which depends on the size of coverage. For small to medium-sized coverage, CRX is given as......(9) where hrx[m] is the height of transmit antenna. For large-sized coverage, CRX depends on the range of the carrier frequency, for example, Figure 4 Log-normal shadowing Path-Loss model IV. OKUMURA/HATA MODEL The Okumura model has been obtained through extensive experiments to compute the antenna height and coverage area for mobile communication systems. The Okumura model was 40...(10) Meanwhile, the path loss at distance d in suburban and open areas are respectively given by the Hata model as...(11)
Path loss Prediction Models for Wireless Communication Channels and its Comparative Analysis Parameters for IEEE 802.16d type A, B, and C models Parameter Type A Type B Type C and...(12) Figure5 presents the path loss graphs for the three different environments urban, suburban, and open areas. It is clear that the urban area gives the most significant path loss. 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 receive antenna, given as... 1.15 or...16 Figure 5 Hata Path-Loss model V. IEEE 802.16D MODEL IEEE 802.16d model is based on the log-norm al shadowing path loss model. There are three different types of models (Type A, B, and C), depending on the density of obstruction between the transmitter and receiver (in terms of tree densities) in a macro-cell suburban area. Referring to [8 11], the IEEE 802.16d path loss model is given as The correlation coefficient in Equation (1.15) is based on the measurements by AT&T while the one in Equation (16) is based on the measurements by Okumura. Figure 6 shows the path loss by the IEEE 802.16d model at the carrier frequency of 2 GHz, as the height of the transmit antenna is varied and the height of the transmit antenna is fixed at 30m..(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 models as given in Table 3, and h TX is the height of transmit antenna (typically, range d from 10 m to 80 m). TABLE-2 Types of IEEE 802.16d path loss models Type Description A B C Macro-cell suburban, ART to BRT for hilly terrain with moderate-to-heavy tree densities Macro-cell suburban, ART to BRT for intermediate path loss condition Macro-cell suburban, ART to BRT for flat terrain with light tree densities Furthermore, Cf is the correlation coefficient for the carrier frequency fc [MHz], which is given as Figure 6 IEEE 802.16d Path-Loss model Note that when the height of the transmit antenna is changed from 2m to 10m, there is a discontinuity at the distance of 100m, causing some inconsistency in the predict ion of the path loss. It implies that a new reference distance d0` must be defined to modify the existing model [9]. The new reference distance d0` is determined by equating the path loss in Equation (12) to the free-space loss in Equation (3), such that...14 TABLE-3 41
International Journal of Engineering, Management & Sciences (IJEMS) ISSN-2348 3733, Volume-2, Issue-3, March 2015 every path may have different path loss since the surrounding environments may vary with the location of the receiver in practice. 3.When dealing with a more realistic situation, log-normal...(17) shadowing model is useful i.e., it allows the receiver at the Solving Equation (17) for d0`, the new reference distance is same distance d to have a different path loss, which varies found as with the random shadowing effect Xσ. 4.The most frequently adopted path loss models is The Okumura...(18) Substituting Equation (1.18) into Equation (1.12), a modified model, it can predict path loss in an urban area. Modifided form is now known as the Hata model. IEEE 802.16d model follows as 5.The IEEE802.16d model is based on the log-normal shadowing path loss model considering the density of obstruction between the transmitter and receiver (in terms of tree densities) in a macro-cell suburban area. 19 Figure7 shows the path loss by the modified IEEE 802.16d model ACKNOWLEDGEMENT The work presented in this paper has been done in M. Tech as part of Thesis at department of Electronics & communication, RCEW, Jaipur, RTU Kota. REFERENCES Fig. 7 Modified IEEE 802.16d Path-Loss model VI. CONCLUSION In this work three different propagation models were studied in various frequency bands and were compared to each others. Each one of them have their advantages and disadvantages based on the calculations carried out and the environmental conditions taken into account. In all, we can say that Hata model is the most used model out of all as this model takesinto consideration various environmental conditions, including the various types of area. 1.The free-space propagation model is used for predicting the received signal strength in the line-of-sight (LOS) environment where there is no obstacle between the transmitter and receiver. 2.The log-distance model is constructed by modifying the free-space path loss with the path loss exponent n that varies with the environments. The limitation with the above two models is that they do not take into account the condition that 42 [1] Ekiz, E.; Sokullu, R., "Comparison of path loss prediction models and field measurements for cellular networks in Turkey,", 2011 International Conference 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 the Okumura-Hata model for the 900 MHz band in Egypt,", 2013 Third International 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, "A measurement-based path loss model for wireless links in mobile ad-hoc networks (MANET) operating in the VHF and UHF band,", 2012 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC), vol., no., pp.349,352, 2-7 Sept. 2012 [4]Dotche, K. A; Diawuo, K.; Ofosu, W. K., "Effect of path loss model on received signal: Using Greater Accra, Ghana as case study," Wireless Telecommunications Symposium (WTS), 2012, vol., no., pp.1,6, 18-20 April 2012 [5]Nisirat, M.A; Ismail, M.; Nissirat, L.; AlKhawaldeh, S.; Yuwono, T., "A Hata based model utilizing terrain roughness correction formula,", 2011 6th International Conference on Telecommunication Systems, Services, and Applications (TSSA), vol., no., pp.284,287, 20-21 Oct. 2011 [6]Ge, Yiqun; Shi, Wuxian; Sun, Guobin, "Impacts of Different SUI Channel Models on Iterative Joint Synchronization in Wireless-MAN OFDM system of IEEE802.16d,", 2005 6th IEE International Conference on 3G and Beyond, vol., no., pp.1,4, 7-9 Nov. 2005 [7]Tan, S.Y.; Tan, M. Y.; Tan, H. S., "Multipath delay measurements and modeling for interfloor wireless communications,", IEEE Transactions on Vehicular 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-Domain technique,", 2008. CEM 2008. 2008 IET 7th International Conference on Computation in Electromagnetics, vol., no., pp.98,99, 7-10 April 2008 [9]Maitham Al-Safwani and Asrar U.H. Sheikh, Signal Strength Measurement at VHF in the Eastern Region of Saudi Arabia, The Arabian Journal for Science and Engineering, Vol. 28, No.2C, pp.3-18, December 2003. [10] Rautiainen, T.; Wolfle, G.; Hoppe, R., "Verifying path loss and delay spread predictions of a 3D ray tracing propagation model in urban environment," Proceedings. VTC 2002-Fall. 2002 IEEE 56th Vehicular Technology Conference, 2002., vol.4, no., pp.2470,2474 vol.4, 2002 [11] Karedal, J.; Czink, N.; Paier, A; Tufvesson, F.; Molisch, AF., "Path Loss Modeling for Vehicle-to-Vehicle Communications," Vehicular Technology, IEEE Transactions on, vol.60, no.1, pp.323,328, Jan. 2011
Path loss Prediction Models for Wireless Communication Channels and its Comparative Analysis [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 for wireless channels in suburban environments,", IEEE Journal on Selected Areas in Communications, vol.17, no.7, pp.1205,1211, Jul 1999 [13] Singh, Y. "Comparison of Okumura, Hata and COST-231 Models on the Basis of Path Loss and Signal Strength." International Journal of Computer Applications (0975-8887) 59.11 (2012). 43