Performance Analysis of Different Channel Models In Wireless Communications

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ternational Journal of Engineeri ng Trends and Technology (I JETT) - Volume4Issue4- Apri l 2013

ISSN: 2231-5381 http://www.ijettjournal.org Page 1279

Performance Analysis of Different Channel Models In

Wireless CommunicationsAnil Kumar Vanarasi

#, Ramesh Pillem

*

#Final Year B.Tech, Dept. of ECE, KL University, Vaddeswaram, AP, India

*Assistant Professor, Dept. of ECE, KL University, Vaddeswaram, AP, India

Abstract - Slow fading channels are of great

importance in communication. In this paper, wefirst have to build a wireless communication

simulator including Gray coding, modulator,

different channel models (AWGN, flat fading and

frequency selective fading channels), channel,

adaptive equalizer and demodulator. Next, we

tested the effect of different channel models to thedata, and image receiver, and BER (Bit Error Rate)

plots of the individual channels for different signal-to-noise ratio (SNR) among 8PSK modulation.

Finally, we provide detailed results and analyse theperformance improvement with channel estimation

and adaptive equalization in slow Rayleigh fadingchannel. For frequency selective fading channel,

we use linear equalization with both LMS (least

mean squares) and RLS (Recursive Least Squares)

algorithm to compare the different improvements.

Rayleigh fading channels are affected by noise,noise due to phase distortion and inter-symbol

interference (ISI).

Keywords: Slow fade, flat fading, fading

frequency, channel estimation, LMS, RLS, bit

error rate, inter-symbol interference, Rayleigh

fading channels, Signal to Noise Ratio..

1. INTRODUCTION

Mobile and wireless network have experienced

massive growth and economic success in recentyears. However, the wireless channels are not as

friendly as wired in mobile radio systems. Unlike

wired channels which are stationary and

predictable, wireless channels are extremely

random and time-variant. It is known that thewireless multi-channel any time dispersion, caused

attenuation and phase shift, known as fading, in thereceived signal [1]. Fading caused by interference

between two or more versions of the transmitted

signal that arrive at the receiver at slightly differenttimes [2-3].

There are many techniques to address the diversity

fading problem, such as OFDM, MIMO, rake

receiver, and etc. However, it may still be necessary

to remove the amplitude and the phase shift caused

by the channel when linear modulation methods,

such as those used in WiMAX. The function of thechannel estimation, an estimate of the amplitude

and the phase shift caused by the wireless channelare available information form pilot. Pilot-based

estimation and blind estimation: Channel estimation

methods can be divided into two classes like this.

In our project, we will focus on pilot-based channel

estimation using training data. The equalization

removes the effect of the radio channel and allows

subsequent symbol demodulation. An adaptiveequalizer is a time-varying filter which will be

permanently housed. A number of different

algorithms are used for these modules. In thiswork, we use LMS (least mean squares) and RLS

(Recursive Least Squares) [4-5].

Digital communication systems, often using time-

varying dispersive channels to send a signal format

in which customer data are organized in blocks of a

known training sequence ahead, the training

sequence can be used at the beginning of each block

in order to train an adaptive channel estimation andequalizer. Depending on the speed with which the

channel varies with time, it may or may not need tofurther track the channel variations during the

customer data sequence.


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I nternational Journal of Engineeri ng Trends and Technology (I JETT) - Volume4Issue4- Apri l 2013


Figure 1 show the flowchart Mat our laboratorysimulation that is used in this work.

2. SYSTEM MODEL

2.1 Channel Modelling

The flat fading channel and frequency selectivefading channel uses parameters like transmitted

symbol period Ts, coherence time Tc and RMS

delay spread . For slow fading channel Ts > , which implies

that for slow flat fading channel


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one of these conditions is met. We can choose to

reset the estimated weights of an equalizer or notbefore starting a new training cycle equalizer at the

next time coherence. Before reset when set filtering

property to 1, while each coherence equalizer is the

equalizer of the state are the results of the previoustraining of the coherence time. If 0, use the

equalization of the result of the last coherence time

of training mode either directed or decision mode

[11-12].

3. SIMULATION AND EXPERIMENTAL

RESULTS :

We discuss our simulation results through two

steps. First, we analyse the performance

comparison of different parameters in each channel.

Then we analyse the performance by comparing

three different channels under the same parameter

setting. All simulations are based on 8PSKmodulation with Gray code.

3.1 For AWGN Channel

1.BER of simulation vs. theoreticalAs shown in FIG. 2 shows the BER performance

simulation results are exactly the same theoretical

BER.

Fig. 2: BER of Simulation vs. Theoretical

2. Image quality of received vs. original

In Fig. 3, the received image plot at SNR = 5 dB,

we see that there is some random noise in theimage. From simulation results, the received image

quality is almost the same as in a SNR = 10dB.

3. BER of Image vs. random dataThe correlation between image pixels does notaffect the BER in AWGN channel.

Fig3.(a)

(b)Figure 3. (a) Original (b) The image quality of the

received coherence time. If set to back up before

the filtering property to 1, while each coherence

equalizer sets the state of the equalizer of the

training results of the last coherence time coming.

If 0, use the equalization of the result of the last

coherence time of training mode either directed or

decision mode


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3.2 For Flat Fading Channel

1. BER of simulation vs. theoretical

Fig.4:BER of Simulation vs. Theoretical

As shown in FIG. 4 shows the BER

performance simulation result worse than

theoretical BER. This makes sense, since the

theoretical BER, based on the assumption that we

know exactly the phase information of the

modulated signal. Due to the time-variant channel

estimation error, we always phase information. We

also find the BER performance is dramaticallyimproved in low SNR, although not in high SNR.

This is also useful because at low SNR white

Gaussian noise dominates the BER errors

promoting SNR can be improved while at high

SNR estimation phase errors dominate the BER

error that cannot be improved simply by increasing

SNR.

2. Image quality of received vs. adjusted

In Fig. 5, the received image plot at SNR = 10 dB,we see that except for some random noise, there is

some block noise in the picture. m. This is due to

the coherence time of a phase difference estimation

error.

Fig 5(a)

(b)Fig. 5: (a) Without Adjustment (b) with Adjustment

3. BER of Image vs. random data

The correlation between image pixels does notaffect the BER in flat fading channel.

3.3 For Frequency Selective Fading Channel

1.BER of simulation vs. theoreticalAs shown in FIG. 6 show the BER performance

simulation result worse than theoretical BER. The

reason is the same for the above mentioned reason

in flat fading channel directed. Unlike flat fadingchannels, the BER performance drastically low

SNR is improved while high SNR even

degradation. This is also useful because in high

SNR, phase estimation error and ISI dominate the

BER error and the estimation error is even severe

ISI, cause even worse the BER cause

2. LMS vs. RLS:


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The BER performances are nearly the same for

both. But during the simulation, we find LMS need

more training data to converge on the equalizer

compared to RLS, while the latter is more complex

and time consuming.

3. Image quality of received vs. originalFigure 7 shows the received image plot is the SNR

= 15dB, we see that other than a random noise and

block noise in the picture, there is some overlap in

the image. This is caused by frequency-selective

fading channel due to the white Gaussian noise

phase estimation error in a coherence time and ISI.

Fig. 6: BER of Simulation vs Theoretical

Fig. 7(a)

(b)

(c)

(d)Fig. 7 (a): Without Equalization (LMS) (b) With Equalization

(LMS) (c) Without Equalization (RLS) (d) With Equalization

(RLS)


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4. BER of Image vs. random dataThe correlation between pixels does not affect theBER in selective fading channel, since PN code we

use to train the equalizer.

4. CONCLUSIONS

From Figs. 3, 5 and 7, one can see that in

AWGN channel, the image degraded by noise. In

flat fading channel, the image degraded by random

and block noise. In frequency-selective fading

channel, the image is affected by noise, block noise

and overlap.

From Figs. 2, 4 and 6 we see the BER

performance is best in AWGN channel, worse inflat fading channel and the worst in selective fading

channel. They are just like the theoretical analysis.

In this paper, we test the effect of three different

channel models, AWGN channel, flat fading

channel and frequency selective fading channel, and

the image data under two scenarios. We also

compare and analysis to improve the channel

estimation and adaptive equalization in slow fading

channel.

REFERENCES

[1] T.S. Rappaport, Mobile Radio Propagation: Small-

Scale Fading and Multipath, in WirelessCommunications: Principles andPractice, 2nd ed.,

Upper Saddle River, N.J., Prentice Hall PTR,2002,pp. 205-223, 2002.

[2] M.M. Salah, A.A. Elrahman, Coded OFDMScheme for Image Transmission Over Time-varyingMultipath Rayleigh Fading Channel, in IEEE

Mediterranean Electro technical Conf.WirelessCommun., pp. 602-607, April 2010.

[3] J.Bhalani, A.I. Trivedi, Y.P. Kosta, PerformanceComparison of Nonlinear and AdaptiveEqualization Algorithms for Wireless Digital

communication. in First Asian Himalayas Int.Conf.Wireless Commun., pp. 1-5, Nov. 2009.

[4] Monsen P., Adaptive Equalization of the

SlowFading Channel, IEEE Trans., IT-22, pp. 1064-1075, Aug. 1974.

[5] Muhammad Islam, M.A. Hannan, S.A. Samad, A.Hussain,Performance of RFID with AWGN andRayleigh FadingChannels for SDR Application,

Lecture Notes in Engineeringand Computer Science,Vol. 2183, pp. 754-758, June 2010.

[6] S. Haykin, Adaptive Filter Theory,Fourth Edition,

2002.

[7] A.V. Oppenheim, R.W. Schafer, J.R. Buck,

Discrete-time.

Performance Analysis of Different Channel Models In Wireless Communications

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