International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue4- April 2013
Performance Analysis of Different Channel Models In
Wireless Communications
Anil 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, we
first 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 the
data, and image receiver, and BER (Bit Error Rate)
plots of the individual channels for different signalto-noise ratio (SNR) among 8PSK modulation.
Finally, we provide detailed results and analyse the
performance improvement with channel estimation
and adaptive equalization in slow Rayleigh fading
channel. 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..
between two or more versions of the transmitted
signal that arrive at the receiver at slightly different
times [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 the
channel estimation, an estimate of the amplitude
and the phase shift caused by the wireless channel
are 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 adaptive
equalizer is a time-varying filter which will be
permanently housed.
A number of different
algorithms are used for these modules. In this
work, we use LMS (least mean squares) and RLS
(Recursive Least Squares) [4-5].
Digital communication systems, often using timevarying dispersive channels to send a signal format
in which customer data are organized in blocks of a
1. INTRODUCTION
known training sequence ahead, the training
Mobile and wireless network have experienced sequence can be used at the beginning of each block
massive growth and economic success in recent in order to train an adaptive channel estimation and
years. However, the wireless channels are not as equalizer. Depending on the speed with which the
friendly as wired in mobile radio systems. Unlike channel varies with time, it may or may not need to
wired channels which are stationary and further track the channel variations during the
predictable, wireless channels are extremely customer data sequence.
random and time-variant. It is known that the
wireless multi-channel any time dispersion, caused
attenuation and phase shift, known as fading, in the
received signal [1]. Fading caused by interference
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue4- April 2013
1. Convergence rate: This is defined as the
number of iterations of the algorithm is
necessary in response to steady state inputs
that converge close enough to the optimal
solution. A fast convergence rate allows the
algorithm to quickly respond to a stationary
unknown environment statistics. Moreover,
it allows the algorithm to statistical
fluctuations on when. In a non-stationary
environment.
Figure 1 show the flowchart Mat our laboratory
simulation that is used in this work.
2. SYSTEM MODEL
2.1 Channel Modelling
The flat fading channel and frequency selective
fading channel uses parameters like transmitted
symbol period Ts, coherence time Tc and RMS
delay spread . For slow fading channel Ts << Tc
and for flat fading channel Ts >> , which implies
that for slow flat fading channel  << Ts << Tc. For
frequency selective fading channel Ts < , which
implies that for slow frequency selective fading
channel Ts <  < Tc. Clarke and Gans fading model
is used for modeling the fading channels with
Rayleigh distribution. A sequence of data (pilot
data) already known to the receiver is transmitted
through the channels and the error in the channels is
estimated by calculating BER of the received data
for various SNR [6-8]. This method of estimation is
called pilot estimation. The estimated error in the
received data is eliminated by process of channel
equalization.
2.2-Channel equalizer
The performance of a calculation algorithm is
determined by several factors including:
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2. Incorrect setting: an algorithm of interest for
these parameters provide a quantitative
measure of the amount by averaging the
final value deviates the mean square error
within a group of adaptive filters from the
optimum minimum mean square error.
3. Computational Complexity: This is the
number of operations required to make a
complete iteration of the algorithm.
4. Numerical properties: If an algorithm can be
implemented numerically inaccuracies due
to rounding produces a noise and display
errors in the computer. These kinds of
errors affect the stability of the algorithm.
The radio channel and use of the subscriber
equipment is another key issue. The speed of the
mobile unit determines the channel fading rate and
the Doppler spread, which is directly related to the
coherence of the channel. Algorithms for adaptive
equalizer can be addressed in decisions adjust mode
indicated by the use of a detector or property
equalizer in training mode with just only pilot data
to train the detector. In the decision directed mode,
Equalizer uses a captured version of its output
signal when adjusting the weights [9-10].
Start adaptive equalizer usually with training and
change in decisions directed mode after all symbols
in the training sequence. Not the syntax of a
training sequence or the equalizer has exhausted all
symbols in the training sequence and still has more
input to process symbols: The function works in
decision-directed phase compensation mode when
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one of these conditions is met. We can choose to
reset the estimated weights of an equalizer or not
before 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 previous
training 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
RESULTS :
AND
In Fig. 3, the received image plot at SNR = 5 dB,
we see that there is some random noise in the
image. From simulation results, the received image
quality is almost the same as in a SNR = 10dB.
3. BER of Image vs. random data
The correlation between image pixels does not
affect the BER in AWGN channel.
EXPERIMENTAL
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 8PSK
modulation with Gray code.
3.1 For AWGN Channel
1. BER of simulation vs. theoretical
As 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
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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 5(a)
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
(b)
also find the BER performance is dramatically Fig. 5: (a) Without Adjustment (b) with Adjustment
improved in low SNR, although not in high SNR.
3. BER of Image vs. random data
This is also useful because at low SNR white
Gaussian noise dominates the BER errors The correlation between image pixels does not
affect the BER in flat fading channel.
promoting SNR can be improved while at high
SNR estimation phase errors dominate the BER 3.3 For Frequency Selective Fading Channel
error that cannot be improved simply by increasing
1. BER of simulation vs. theoretical
SNR.
As shown in FIG. 6 show the BER performance
simulation result worse than theoretical BER. The
2. Image quality of received vs. adjusted
reason is the same for the above mentioned reason
In Fig. 5, the received image plot at SNR = 10 dB, in flat fading channel directed. Unlike flat fading
we see that except for some random noise, there is channels, the BER performance drastically low
some block noise in the picture. m. This is due to SNR is improved while high SNR even
the coherence time of a phase difference estimation degradation. This is also useful because in high
error.
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. original
Figure 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.
(b)
Fig. 6: BER of Simulation vs Theoretical
(c)
(d)
Fig. 7(a)
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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 data
The correlation between pixels does not affect the
BER 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 in
flat 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
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[3] J.Bhalani, A.I. Trivedi, Y.P. Kosta, “Performance
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[4] Monsen P., “Adaptive Equalization of the
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[5] Muhammad Islam, M.A. Hannan, S.A. Samad, A.
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