propagation characteristics of a mobile radio channel for rural

IPASJ International Journal of Electronics & Communication (IIJEC) Web Site: http://www.ipasj.org/IIJEC/IIJEC.htm

A Publisher for Research Motivation........ Email: [email protected] Volume 2, Issue 9, September 2014 ISSN 2321-5984

Volume 2, Issue 9, September 2014 Page 30

ABSTRACT With the introduction of high speed data transmission over wireless networks, higher carrier frequencies and due to increase in buildings and other obstacles in the environment, transmission losses have been a major parameter while setting up a wireless network. Transmission channel analysis is done before setting up of a wireless network to estimate the parameters like the coverage area, link budget, SNR, cell capacity etc. There are two types of variations of radio signals. First, the long-term variations where the average value of signal depends on its distance, carrier frequency, antenna height, atmospheric conditions and so on which results in loss of signal power at the receiver. The second type of variation, short-term variations, is due to multipath reflections and Doppler and degrades the quality of signal received at the receiver. In this project the Hata-Okumura model was employed for estimating the pathloss experienced by the signal in the wireless channel. The pathloss variation in Rural, Urban and Suburban environments is described with respect to the change in parameters like antenna heights, carrier frequency and separation between transmitter and receiver. Also the sum of sinusoids method is used for evaluating the received signal for various multipath environments and the effect of Doppler spread on the signal and generated Rayleigh and Rician channel. Keywords: Pathloss models, Propagation characteristics, Large scale variations of Wireless networks, Short term variations of Wireless networks.

1. INTRODUCTION Large-scale variations can be observed in a signal over large distances. Received power or its reciprocal, pathloss, is generally the most important parameter predicted by large scale propagation models. Large-scale variations in a signal are mainly due to Pathloss and shadowing. Pathloss is caused by dissipation of the power radiated by the transmitter as well as by effects of the propagation channel. Path-loss models generally assume that pathloss is the same at a given transmit–receive distance (assuming that the path-loss model does not include shadowing effects)[1]. Shadowing is caused by obstacles between the transmitter and receiver that attenuate signal power through absorption, reflection, scattering, and diffraction. When the attenuation is strong, the signal is blocked. Received power variation due to pathloss occurs over long distances, whereas variation due to shadowing occurs over distances that are proportional to the length of the obstructing object. In urban or dense urban areas, there may not be any direct line-of-sight path between a mobile and a base station antenna. Instead, the signal may arrive at a mobile station over a number of different paths after being reflected from tall buildings, towers, and so on. Because the signal received over each path has a random amplitude and phase, the instantaneous value of the composite signal is found to vary randomly about a local mean. Since these variations are rapid and occur over short distances these variations are termed as short term variations[3]. The prediction of pathloss is a very important step in planning a mobile radio system, and accurate prediction methods are needed to determine the parameters of a radio system which will provide efficient and reliable coverage of a specific service area. Earlier the factors influencing the radio signal are explained and with that knowledge we develop a mobile propagation model. The mobile radio channel is usually evaluated from 'statistical' propagation models: Three types of channel models are proposed to model wireless channels: Empirical channel models, Statistical channel models and Semi-empirical models. Out of these models, Empirical channel models are derived based on a large amount of experimentally obtained data. These models have a higher efficiency but are complex to design as the variable parameters increase. The area mean is directly related to the pathloss, which predicts how the area mean varies with the distance between the BS and MS. Early studies by Okumura and Hata yielded empirical pathloss models for urban, suburban, and rural areas that are accurate to within 1 dB for distances ranging from 1 to 20 km and this analysis is best suitable for Large scale analysis of mobile radio signal. In this, Statistical

Propagation Characteristics of a Mobile Radio Channel for Rural, Suburban and Urban

Environments Mr. ANIL KUMAR KODURI1, Mr. VSRK. SHARMA2, Mr. M. KHALEEL ULLAH KHAN3,

1 STUDENT, M.TECH 2,3 ASSOCIATE PROFESSOR

DEPARTMENT OF ECE, KRISHNA MURTHY INSTITUTE OF TECHNOLOGY & ENGINEERING




channel models are derived by assuming various probability distributions to the signal parameters like the angle of arrival, path delays etc. such models have lower accuracies but are easy to derive and may aid in estimation of channel performance. In this paper, we used Rayleigh channel model for the analysis of Multipath environment, Rician channel model has been used for Doppler spreads.

2. CHANNEL MODELS FOR LARGE SCALE ANALYSIS Large scale variations are very slow and are calculated over a large area. These variations are generally assumed to have lognormal distribution. Empirical channel models have been the best for analysis of large scale variations in this section; we present some models which we will use in this paper for large scale variation analysis[3]. 2.1 HATA-OKUMARA MODEL The Hata-okumura model is a version developed for use in computerized coverage prediction tools. Hata obtained mathematical expressions by fitting the empirical curves provided by Okumura[4]. Expressions for calculating the pathloss, L (dB) (between isotropic antennas) for urban, suburban and rural environments are provided. For flaturban areas,

(2.1)

where is in MHz, and are in meters and d in km. Parameter is the BS effective antenna height and

is the MS height, and d is the radio path length. For an MS antenna height of 1.5 m, . Model corrections are given next. The values of A , n are determined by the operating frequency, antenna heights and other influencing factors. For example, if the base station antenna height is 50 m and the mobile antenna height 1.5 m, the model gives the following pathloss at 900 MHz for a typical urban area:

(2.2) Notice that the pathloss at 1 km from the transmitter is 123.33dB. Similarly, the pathloss for the same antenna heights at 1,900MHz is given by

(2.3) The pathloss in suburban and open areas is less than in urban areas. For example, at 1,950 MHz, this improvement in pathloss isabout 12 dB for suburban and 32 dB for open areas. Corrections for determining pathloss in suburban and

rural areas are also determined by Hata as for a medium-small city,

(2.4) For a large city,

(2.5)

(2.6) For a suburban area,

(2.7)

For rural areas,

(2.8) The model is valid for the following range of input parameters:



Volume 2, Issue 9, September 2014 2

Some years ago, in view of the need to deploy higher frequency systems, such as the GSM at 1800 MHz or PCS at 1900 MHz, a new revision of the Hata model (COST 231-Hata) Coverage and Interference 65 was developed using similar methods to those used by Hata. The COST 231-Hata model follows the expression:

(2.9) where has the same expression as in the original model for a medium-small city and Cm is equal to 0 dB for medium-size cities and suburban cities, and equal to 3 dB for metropolitan cities. The validity of this modification is the same as for the original model, except for the frequency range which 2.2 SIGNAL VARIATIONS COMPARISON IN RURAL, SUBURBAN AND URBAN AREAS In propagation studies for mobile radio, a qualitative description of the environment is often employed using terms such as rural, suburban, urban and dense urban. Dense urban areas are generally defined as being dominated by tall buildings, office blocks and other commercial buildings, whereas suburban areas comprise residential houses, gardens and parks. The term ‘rural’ defines open farmland with sparse buildings, woodland and forests. So far, we have only discussed signal variations in urban areas. Because the effect of the environmental clutter in suburban or rural areas is not as severe, the average signal level in these areas is comparatively better[2]. This improvement in the signal levels increases with frequencies, but does not appear to depend on the distance between base stations and mobile terminals or on the antenna heights. With the help of some examples evaluated, we can observe the variation of signal levels in rural, sub-urban and urban areas. Improvement in signal level with distance According to Hata model, For an transmitting antenna height ht =30.48 m; Receiving antenna height hr = 3m; Carrier frequency fc = 850Mhz Pathloss in urban areas Pathloss in rural areas

(2.10) Pathloss in sub-urban areas

(2.11) Where, d is in Km

(2.12)

0 1 2 3 4 5 6 750

100

150

200

250

300

350

400

path

loss

(db)

distance(km)

Urban

Rural

Sub-urban

Figure 2.1: Comparison between pathloss in urban, sub urban and rural areas with change in distance

Improvement in signal level with antenna height: For a Receiving antenna height hr = 3m; Distance between transmitter and receiver d = 20km Carrier frequency

Pathloss in urban areas

(2.13) Pathloss in rural areas




Pathloss in sub-urban areas

Where is transmitter height in meters

0 20 40 60 80 100 120 140 160 180 20060

80

100

120

140

160

180

200

220

path

loss

(db)

antena height(m)

Urban

Rural

Sub-urban

Figure 2.2: Comparison between pathloss in urban, sub urban and rural areas with change in antenna heights

Improvement in signal level with frequency: For a transmitting antenna height ht = 30.48 m Receiving antenna height hr = 3m Distance between transmitter and receiver d = 20km Pathloss in urban areas

Pathloss in rural areas

Pathloss in sub-urban areas

800 900 1000 1100 1200 1300 1400 1500120

140

160

180

200

220

240

260

280

300

path

loss

(db)

frequency(Mhz)

Urban

Rural

Sub-urban

Figure 2.3: Comparison between pathloss in urban, sub urban and rural areas with change in carrier frequencies

Signal level improvement is high with change in frequency when compared with change in other parameters in rural and urban areas[6].




3. CHANNEL MODELS FOR SHORT TERM ANALYSIS Short term variations are are rapid and occur over short distances. These variations are generally assumed due to Multipath environment and Doppler spreads. Statistical channel models have been the best for analysis of Short term variations in this section; we present some models which we will use in this paper for Short term variation analysis[8]. 3.1 SIMULATION MODEL OF A MOBILE RADIO CHANNEL Consider the transmission of the band-pass signal

where is the channel response at time t due to an impulse applied at time and is the dirac delta function. When the Line of sight component is present, the signal is assumed to undergo fading which follows Rician distribution and when the Line of sight component is absent, the signal undergoes fading corresponding to Rayleigh distribution. In the next section we present these distribution functions and equations for Rayleigh and Rician channel transfer functions. 3.2. RAYLEIGH FADING CHANNEL The path between the base station and mobile stations of terrestrial mobile communications is characterized by various obstacles and reflections[2]. The general characteristics of radio wave propagation in terrestrial mobile communications are shown in Figure 3.1. The radio wave transmitted from a base station radiates in all directions these radio waves, including reflected waves that are reflected off of various obstacles, diffracted waves, scattering waves, and the direct wave from the base station to the mobile station. In this case, since the path lengths of the direct, reflected, diffracted, and scattering waves are different, the time each takes to reach the mobile station will be different.

Figure 3.1: Principle of multipath channel.




In addition the phase of the incoming wave varies because of reflections. As a result, the receiver receives a superposition consisting of several waves having different phase and times of arrival[7]. The generic name of a radio wave in which the time of arrival is retarded in comparison with this direct wave is called a delayed wave. Then, the reception environment characterized by a superposition of delayed waves is called a multipath propagation environment. In a multipath propagation environment, the received signal is sometimes intensified or weakened. This phenomenon is called multipath fading, and this section discusses the concept of multipath fading and explains a programming method for simulations of multipath fading[8]. Let us begin with the mechanism by which fading occurs. The delayed wave with incident angle n is given by (3.7) corresponding to Figure 3.1, when a continuous wave of single frequency fc (Hz) is transmitted from the base station.

where Re[ ] indicates the real part of a complex number that gives the complex envelope of the incoming wave from the direction of the number n. Moreover, j is a complex number. en(t ) is given in (2.10) by using the propagation path length from the base station of the incoming waves: Ln (m), the speed of mobile station, v (m/s), and the wavelength, λ(m).

where Rn and fn are the envelope and phase of the nth incoming wave. xn(t ) and yn(t ) are the in-phase and quadrature phase factors of ten , respectively. The incoming nth wave shifts the carrier frequency by the Doppler effect. This is called the Doppler shift in land mobile communication. This Doppler shift, which is described as fd, has a maximum value of n/l, when the incoming wave comes from the running direction of mobile station in cosθn = 1. Then, this maximum is the largest Doppler shift. The delayed wave that comes from the rear of the mobile station also has a frequency shift of -fd (Hz). It is shown by (4.9), since received wave r(t) received in mobile station is the synthesis of the above-mentioned incoming waves, when the incoming wave number is made to be N.

where x(t ) and y (t ) are given by

and x(t) and y(t) are normalized random processes, having an average value of 0 and dispersion of σ, when N is large enough. We have (3.24) for the combination probability density p(x, y), where x = x(t), y = y (t)

In addition, it can be expressed as r (t) using the amplitude and phase of the received wave.

R(t) and are given by

By using a transformation of variables, p(x,y) can be converted into p(R, )

By integrating p(R, ) over q from 0 to 2, we obtain the probability density function p(R)




Moreover, we can obtain the probability density function p( ) by integrating p(R, ) over R from 0 to

From these equations, the envelope fluctuation follows a Rayleigh distribution, and the phase fluctuation follows a uniform distribution on the fading in the propagation path[6]. Next, let us try to find an expression for simulations of this Rayleigh fading. Here, the mobile station receives the radio wave, the arrival angle of the receiving incoming wave is uniformly distributed, and the wave number of the incoming waves is N. In this case, the complex fading fluctuation in an equivalent lowpass system is,

Consider an unmodulated signal S(t)=cos(2πfct). Let αn be the angle of arrival of nth ray at the receiver. Φn be the

phase shift of nth ray due to multipath. v be the velocity of the receiver and wd be the Doppler shift. Assume there are N scaterers in the environment. The received signal will be of the form

We consider the second product term as the low pass equivalent response of the channel and is evaluated as

It should be obvious at this point why simulators which produce signals of the form of equation, or equivalently equation, are called sum-of-sinusoids simulators[2]. The distinguishing feature of this type of simulator is that it contains a low-frequency oscillator for each Doppler shift, wn=wdcos(αn), i.e., is made up of N oscillators. A general relation between the Cn’s and the pdf of the angles of arriva1 is supplied by Jakes as

where f(αn) is the pdf of the nth angle of arrival and the cn's may be interpreted as the power ratio received within the small arc dαn, about the angle of arriva1αn.The first step taken by Jakes is to restrict the angles of arriva1 from being uniform i.e, fα1(α1)= fα2(α2)= ……….=fαN(αN)=1/2π to be uniformly spaced. And according to the formula

This, in turn, leads to the attenuation along the N paths being equal, i.e,

This amounts to observing that since the pdf of the angles of arrival f(αn) is uniform, the power received in each arc dαn, is the same, as long as the αn,are uniformly spaced. So equation (3.20) becomes




When expanded the equation becomes

Where n and n are uniformly distributed over 0 to 2π. Wd=Doppler frequency shift and N=number of scaterers. The Doppler frequency shift is calculated by

Where v=mobile velocity, λ=wavelength of the carrier signal. The level crossing rate and average fade duration are given by the formula

Where ρ=fade level

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

time(seconds)

Nor

mal

ised

am

plitu

de

Figure 3.2: Rayleigh channel response for a relative velocity of 50m/s generated by sum of sinusoids method

We can calculate response of channel for different velocities of receiver by changing the value of wd in the MATLAB code according to the equation 3.26.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

3

time(seconds)

Nor

mal

ised

am

plitu

de

Figure 3.3: Rayleigh channel response for a relative velocity of 100m/s generated by sum of sinusoids method




Observations- The level crossing rate (LCR) for a velocity of 100m/s is more than the level crossing rate for a velocity of 50m/s for a fade level of 1. The average fade duration (AFD) for a velocity of 100m/s is more than the level average fade duration for a velocity of 50m/s for a fade level of 1 3.2 SPECIAL CASE OF RAYLEIGH FADING SIMULATOR For the simulation equation mentioned in 3.27, there is an exceptional case when the mobile receiver is stationary (wd=0). For simplicity of implementation, we have assumed both the angles of arrival and the phase shift due to Multipath are assumed to be uniformly distributed over 0 to 2π[2]. But in case when wd=0, 3.36 reduces to

We can observe that the equation is independent of time. This shows that when the receiver is stationary, the received signal experiences a constant attenuation and a constant phase shift due to multipath components. If uniform distribution of n is assumed in this case, the multipath components cancel each other for even number of scaterers and resulting in 0 received signal. So we simulate this case by assuming to have N random values between 0 to 2π. Figure 3.4 shows the comparison between transmitted and received signals when only multipath effect is present.For such a system, the received output is an attenuated and phase shifted version of the original wave. For an input signal, Tx(t)= cos(2πfct) ; The received signal through the channel simulated is ; Rx(t)= 0.66 cos(2πfct-0.334)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

x 10-3

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

time(seconds)

norm

alis

ed a

mpl

itude

transmitted signal

recieved signal

Figure 3.4: Received and transmitted signals when receiver is stationary

3.3. RICIAN CHANNEL Rician distribution is used to model the channel when a direct line of site component exists between the transmitter and receiver in addition with the multipath components[3]. It can be expressed as a phasor sum of a constant and a number of scattering point sources.




Where K is the ratio of multipath to direct component and and are angle of incidence and initial phase of direct component respectively. Table 3.1: Normalised amplitude of the signal at various time instances received at a receiver moving with velocity of

50m/s and k=3 Time(msec) Normalised amplitude of received signal

0.1 1.845 0.2 2.103 0.3 2.066 0.4 1.601 0.5 0.608 0.6 1.984 0.7 2.708 0.8 2.305 0.9 0.8358 1 0.9792

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

3

3.5

time(seconds)

Nor

mal

ised

am

plitu

de

Figure 3.5: Rician channel response for a relative velocity of 50m/s and K=3 generated by sum of sinusoids method




Table 3.2: Normalised amplitude of the signal at various time instances received at a receiver moving with velocity of 50m/s and k=6

Time(msec) Normalised amplitude of received signal 0.1 1.566 0.2 1.63 0.3 1.63 0.4 1.439 0.5 0.5227 0.6 1.591 0.7 2.315 0.8 1.911 0.9 0.5535 1 1.001

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

3

time(seconds)

Nor

amlis

ed a

mpl

itude


Table 3.3: Normalised amplitude of the signal at various time instances received at a receiver moving with velocity of


0.1 1.022 0.2 1.498 0.3 1.177 0.4 1.307 0.5 2.03 0.6 1.103 0.7 0.839 0.8 3.175 0.9 1.744 1 2.264

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

3

time(seconds)

Nor

amlis

ed a

mpl

itude





Table 3.3 Normalised amplitude of the signal at various time instances received at a receiver moving with velocity of


0.1 0.8199 0.2 1.174 0.3 0.8675 0.4 1.291 0.5 1.598 0.6 1.236 0.7 0.978 0.8 2.612 0.9 1.551 1 1.681

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

3

time(seconds)

Nor

amlis

ed a

mpl

itude


4. CONCLUSION In this paper, we have proposed certain empirical models for studying the pathloss and its dependence on various factors like distances between the transmitter and the receiver, antenna heights and frequency. We have simulated the results in MATLAB. We have compared these variations in Urban Rural, and Suburban areas. Also, discussed the short term fluctuations experienced by the signal. In this paper, we have designed a transfer function to analyse the performance of different modulation techniques in a channel and calculate the LCR and AFD. With the knowledge of these parameters, the bit error rate can be calculated and thereby suitable coding scheme can be evaluated. We have also simulated the effect of relative velocity of receiver and transmitter on the performance of the communication system. This pathloss modelling and analysis can be extended easily to satellite wireless communication channel.

REFERENCES [1] T.S Rappaport, “Wireless Communications”, Chs. 3 and 4, Upper Sadle River, NJ: Prentice Hall, 1996. [2] Rayleigh Fading Channels in Mobile Digital Communication Systems, Bernard Sklar, Communication Engineering

Services. [3] D. Greenwood and L. Hanzo, “Characterisation of Mobile Radio Channels”. [4] M. Hata, “Empirical Formulae for Propagation Loss in Land Mobile Radio Services”, IEEE Trans. Vehic. Tech,

Vol. VT-29, No.3, 1980. [5] Ali Abdi, Wing C. Lau and Mostafa Kaveh, “ A New Simple Model for Land Mobile Satellite Channel: First- and

Second-Order Statistics”, IEEE Transactions on Wireless Communications, Vol. 2, No. 3, May, 2003. [6] Simulation and Software Radio for Mobile Communications, Hiroshi Harada, Ranjee Prasad. [7] Gordan L. Stuber, “Principles of Mobile Communication”, Second Edition. [8] Branka Veucetic and Jun Du, IEEE Journal on Selected areas in Communications, Vol. 10, No. 8, October, 1992.




[9] Modelling the Wireless Propagation Channel, F. Pe’rez Fonta’ and P. Martin Espin~ eira, University of Virgo, Spain.

[10] Hess, G.C, “Hand Book of Land-Mobile Radio Sy

propagation characteristics of a mobile radio channel for rural

Documents