International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
Implementation and Evaluation of ANN Assisted
Turbo Coding for use with OFDM Signals in
Wireless Channels
1 Mr Mallikarjun.G.Ganachari, 2 Prof Basavaraj.D
Department of ECE, Vishwanathrao Deshpande Rural Institute of Technology, Haliyal,
Visvesvaraya Technical University, Belgaum, Karnataka, India
Abstract—- This project deals OFDM signal for
transmission in the wireless channel. Wireless
channel coding improves the performance of
OFDM signal, but the system design becomes
complex due to the iterative decoding technique.
ANN Assisted Turbo Coding for use with OFDM
the system design can be simplified and made
faster by the use of ANN in the coding and
decoding process. To analyses the BER
performance Simulation is done in MATLAB.
Keywords- ANN, Turbo Code, OFDMr.
I. INTRODUCTION
The main advantage of using OFDM for
transmitting data over a dispersive channel is that
the output symbols at the OFDM receiver are free
from Inter symbol Interference (ISI). The
performance of an OFDM system in a multipath
fading environment can be further improved by
using a channel coding technique to do away with
the effects of the channel. However, the
improvement in BER comes at a cost of design
complexity in the encoder and decoder. The system
design can be simplified by the use of soft
computing methods like Artificial Neural Network
(ANN). This work deals with an ANN assisted
system for encoding and decoding applications with
OFDM signal in a wireless channel. The BER
performance in AWGN, Rayleigh and Rician fading
channels are considered for Turbo coded signal for
transmission using OFDM.
In modern
communication system, orthogonal frequency
Division Multiplexing (OFDM) is an important
ISSN: 2231-5381
wide band transmission scheme which uses parallel
frequency channels to transmit modulated data.
OFDM has received appreciation in many
applications due to its high data rate and robustness
to time dispersion in multipath fading and simple
equalization at the receiver. OFDM allows many
users to transmit in an allocated band by subdividing the entire bandwidth into several narrow
band channels. The sub-channels are generated such
that they are orthogonal to each other, thus allowing
the sub-channels to be closely spaced in the
spectrum. The overlapping sub-channels make the
OFDM system more spectrum efficient than the
standard frequency division multiplexing (FDM)
system.
II. ANN ASSISTED TURBO CODED OFDM
SYSTEM MODEL
Figure1 shows the block diagram of the
ANN assisted Turbo coded OFDM system of Turbo
coded OFDM system which consists of a simple
transmitter/receiver pair and the propagation
channel, clearly showing how the ANN would fit
into the system. The information data is encoded
using ANN assisted Turbo coder and then
modulated using digital modulation schemes like
M- PSK or M-QAM. The modulated symbols are
allotted to parallel subcarriers before transmission
through the channel. At the receiver end, the
received symbols are OFDM demodulated,
demapped and then decoded to recover the
transmitted data. The decoding process is
performed by ANN to reduce design complexity. A
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
brief overview of each of the components in the
system is presented next ANN assisted Turbo coded
OFDM
system.
Figure1. ANN assisted Turbo coded OFDM system
A. OFDM System
In the OFDM modulator, digitally
modulated information symbols are allotted to
parallel carriers by the serial to parallel converter.
The Inverse Fast Fourier Transform (IFFT) is
performed to convert the time domain signal to
frequency domain before transmission. IFFT
ensures that the carriers are orthogonal to each
other. The parallel output streams from the IFFT
block are converted to serial form and a cyclic
prefix is added to mitigate the dispersive effects of
the channel. At the receiver, the cyclic prefix is
removed before converting the data into parallel
stream. The Fast Fourier Transform (FFT) block
transforms the signal back to time domain signal.
The serial data is then demapped before applying it
to the decoder.
B. Artificial Neural Network (ANN)
ANN is an excellent mathematical tool that
is composed of simple elements, called
neurons
that
can
perform
parallel
operations. It is a network inspired by
Biological nervous systems, which can establish
a relationship between the input and output data by
adjusting its weights through a learning process.
From the structural point of view a Neural Network
may be single layered or it may be multilayered.
Each neuron of one layer is connected to each and
every neuron of the next layer.
C. ANN training and testing:
The learning process used in the work is of
supervised kind and the specific network type is the
Multi Layer Perceptron (MLP) which is a feed
forward structure. The MLP is composed of input
layer, hidden layer and a output layer (Figure5).
Figure 3.Multi-Layer Perceptron
Figure2. DM block diagram
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D. Turbo Encoder
The conventional Turbo encoder consists of
two Recursive Systematic Convolution (RSC)
encoders (Figure4). The input message bits are fed
directly to one of the encoders while an interleaved
sequence of the same is fed to the second encoder.
The encoded sequence consists of the information
bits concatenated with the parity bits from the two
encoders. The encoder can be designed to be either
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
punctured or unpunctured. In the system model
under consideration in this work, rate ½ codes have
been generated by puncturing the parity bits from
the two RSC encoders. The conventional encoder is
then replaced by an ANN that is trained to perform
the encoding process.
Figure5. Iterative decoding scheme
Figure4. Turbo Encoder
E. Turbo Decoder
For a Turbo decoder that uses concatenated
codes, soft decision decoding is used rather than
hard decision decoding. In the decoder being used
for our simulation, where there are two component
codes, the soft decision of one decoder is passed to
the input of the other decoder, and the process is
iterated so as to arrive at a reliable decision. Here
we have considered a Log Map decoder with 10
iterations to achieve a BER of 10 at around 10 db
for faded channel. The complexity -4 of
implementing several iterations to arrive at a
reliable decision can be reduced by implementing
ANN to perform the decoding performance.
F. Wireless Channel
In wireless communication multipath
propagation is a common instance. Fading effect
takes place due to reception of two or more number
of signals at a slightly different time, leading to
change in amplitude, phase and multipath delays of
the transmitted signal, called as fading. Out of the
several kinds of faded channel, the Rayleigh and
Rician faded channel are used in this work.
Rayleigh Faded Channel: The Rayleigh distribution
is given by p(r) as below, where the 1 is the time
average power 2 of the received signal.
P(r) =
e(A2/2
2)
for(0≤r≤∞)
0,
for(r<0)
Rician Faded Channel: The Rician
distribution that is used for LOS communication,
can be described as p(r),
P(r) =
e-(r2+A2/2
2)
I0(A
0,
K=A2/2
)
for(A≥0,r≥0)
for(r<0)
2
Where, the Rician distribution is described
in terms of parameter K, known as Rician factor,
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
which is the ratio between the deterministic signal
power and the variance of the multipath.
III.SIMULATION RESULTS
The performance of the designed system is
evaluated
through
MATLAB
simulation
considering BER as the performance parameter.
The detail of the signal and system
characteristics has been elaborated in this section.
OFDM signal is generated using IFFT technique
which provides an easy method to generate
orthogonal sub-carriers.
Figure 7.BER plot for PSK –OFDM signals in Rayleigh Faded
channel
In AWGN as well as faded channel, the
BER performance is as follows:
BER BPSK < BER QPSK < BER 8PSK < BER 16
PSK
Similar variation is observed in case of MQAM modulated signals. The details of the
conventional Turbo encoder used are tabulated in
form of Table I.
Rate
Type 2 RSC
Generator polynomial
Decoder type
No of iterations
SNR ±10dB
½
Encoders; punctured
G=[1 1 1;1 0 1]
LogMap decoder
10
Table I. Specification of turbo encoder/decoder
Figure6. Spectrum of OFDM signal generated
The BER performance has been observed
for the ODFM signal for M-PSK and M-QAM
digitally modulated signals (M=2, 4, 8, 16) signals
in AWGN, Rayleigh and Rician fading channel
(Figure7).
The Turbo coded OFDM has been
compared with the uncoded OFDM signal in
Rayleigh channel in Figure8 and in Rician channel
in Figure9. In wireless faded channel, use of Turbo
coding in OFDM signal improves the BER for
higher value of SNR, for both Rayleigh and Rician
fading channel.
Figure8. BER plot for OFDM signal and Turbo coded OFDM
signal in Rayleigh faded channel.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
Figure10. Performance plot for ANN decoder
Figure9. BER plot for OFDM signal and Turbo coded OFDM
signal in Rician faded channel.
Two separate ANNs have been trained and
tested to perform the encoding and the decoding
process of Turbo code to be used with the OFDM
signal. Table III shows the details performance
graph of ANN training for the decoder is shown in
Figure10. An improvement in the BER is observed
when the Turbo encoding/decoding is performed
using ANN as compared to iterative decoding
technique (Figure11).
Figure11. BER plot for Turbo coded OFDM signal in
Rayleigh faded channel using iterative decoder and ANN
based decoder
ANN type
Number of Layers
Hidden layer size
Input layer size
SNR consideration
Transfer function
combination
Convergence limit
Training type
Feed forward
One input, one hidden, one output
1.5 times of input
Size of the signal sample at the
receiver end
±10dB
Logsig-tansig-tansig-logsig
104
Back propagation with adaptive
learning rate
IV. CONCLUSION
The performance of the ANN assisted Turbo
coded OFDM is found to be superior compared to
the Turbo coded OFDM with iterative decoding in
terms of BER as well as design complexity and
time of operation. The outcome of ANN based
decoder is more reliable as it can be trained to
mitigate the effect of the faded channel to arrive at
an optimum BER value.
Table II. ANN parameters used for training
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
Acknowledgment
I owe a debt of gratitude to our guide Prof
Basavaraj D VDRIT haliyal who stood as assets in
completion of this project. I thank for their valuable
time and resources.
REFERENCE
[1]Aradhana Misra and Kandarpa Kumar Sarma
Dept. of Electronics and Communication
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Turbo Coding for use with OFD Signals in Wireless
Channels” in Proceedings of of International
Conference on ETNCC-2011 pages:65-69.
[2] J.N. Patel and U.D Dalal: "A Comparative
Performance Analysis of OFDM Using MATLAB
Simulation with M-PSK and M-QAM Mapping," in
Proceedings of International Conference on
Computational Intelligence and Multimedia
Applications, 2007, vol.4, pp.406-410, Dec. 2007
[3] R. Annauth and H.C.S. Rughooputh; “Neural
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3336-3341.,1999
[4] B. Sklar and P.K. Ray; “Digital
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2nd Edition, Pearson,New Delhi,2001
[5] T. .Rappaport; “Wireless CommunicationsPrinciples and Practice,”2nd Edition, PHI, 2002 [
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Foundation”, 2nd Edition, Pearson Education, New
Delhi, 2003.
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