International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 6- May 2015
To Reduce Bit Error Rate in Turbo Coded
OFDM with using different Modulation
Techniques
Shivangi#1, Manoj Sindhwani*2
#1
#2
Department of Electronics & Communication, Research Scholar, Lovely Professional University, Punjab, India
Department of Electronics & Communication, Assistant Professor, Lovely Professional University, Punjab, India
Abstract—
OFDM(Orthogonal
Frequency
Division
Multiplexing) has gathered increased interest due to its high
spectrum efficiency and robustness against multipath
interference, which makes the efficient use of spectrum by
allowing overlap. OFDM is a popular modulation method for the
transmission of high data rates over the wireless channels. There
is a growing demand for the transmission of information quickly
without the formation of errors. In this paper, the use of Turbo
codes increases the reliability of OFDM system to achieve high
data rates. In this project the system throughput of a working
OFDM system has been enhanced by adding turbo coding and
with using different modulation techniques. The use of turbo
coding and power allocation in OFDM is useful to the desired
performance at higher data rates. The purpose of this project is
to increase the system throughput while maintaining system
performance under a desired bit error rate (BER). To improve
the performance of the uncoded OFDM signal by convolution
coding. Simulation is to be done over additive white Gaussian
noise (AWGN) channel.
transmission to digital modulation schemes [1, 2].
OFDM is symbol based, and can be thought of as a
large number of low bit rate carriers transmitting in
parallel. These all carriers transmitted using same
time and frequency, which forms a single block of
spectrum. This is to established that the orthogonal
nature of the structure is maintained [3, 4]. Since
these multiple carriers form a single OFDM
transmission, they are commonly referred to as
‗subcarriers‘. There are several ways of looking at
what make the subcarriers in an OFDM signal
orthogonal and why this prevents interference
between them.
II. TURBO CODES
Keywords— Bit error rate, Orthogonal frequency division
multiplexing, Turbo codes, FEC, Quadrature Amplitude
Modulation.
I.
INTRODUCTION
Orthogonal Frequency Division Multiplexing
(OFDM) is a Multi-Carrier Modulation technique in
which a single high rate data-stream is divided into
multiple low rate data-streams and is modulated
using sub-carriers which are orthogonal to each
other [1]. Some of the main advantages of OFDM
are its multi-path delay spread tolerance and
efficient spectral usage by allowing overlapping in
the frequency domain. Also one other significant
advantage is that the modulation and demodulation
can be done using inverse fast fourier transmission
(IFFT) and fast fourier transmission (FFT)
operations, which are totally efficient.
In a single OFDM transmission all the subcarriers
are synchronized to each other, controlling the
ISSN: 2231-5381
The combination of turbo codes with the OFDM
transmission is so called Turbo Coded OFDM (TCOFDM) can yield significant improvements in
terms of lower energy needed to transmit data, a
very improvement matter is in personal
communication devices [5, 6]. Turbo codes were
first presented at the International Conference on
Communications in 1993. Until then, it was
generally believed that to achieve near Shannon‘s
bound performance, one would ought to implement
a decoder with infinite complexity. Parallel
concatenated codes, as they are also known, can be
implemented by using either convolutional codes
(PCCC) or block codes (PCBC). PCCC resulted
from the combination of three ideas that were
known to all in the coding community:
The transformation of commonly used nonsystematic convolutional codes into
systematic convolutional codes.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 6- May 2015
The utilization of soft input soft output
decoding. Preferably of using hard
decisions, the decoder uses the feasibilities
of the received data to generate soft output
which also contain information about the
degree of certainty of the output bits.
This is achieved by using an interleaver.
Encoders and decoders working on
permeated versions of the same information.
The mixture of turbo codes with the OFDM
transmission is so called Turbo Coded OFDM
(TCOFDM) can yield significant improvements
in terms of lower energy needed to transmit data,
a very improvement issue is in personnel
communication devices.
Fig 1: Structure of a Turbo Encoder
Turbo Decoding
A block diagram of a turbo decoder is shown in
Figure below . The input to the turbo decoder is a
sequence of received code values
= {
}
from the demodulator [8, 9 ,10]. The turbo decoder
consists of two component decoders
to
decode sequences from
, and
to decode
sequences from
.
takes as its input the
received sequence of systematic values
and the
received sequence of parity values
belonging to
the first encoder
. The output of
is a
sequence of soft estimates
of the transmitted
data its .
is called external data, that does
not contain any information which was given to
by
. This information is interleaved, and
then passed to the second decoder
takes as its input the (interleaved) systematic
received values s k y and the sequence of received
parity values
from the second encoder
,
along with the interleaved form of the extrinsic
information
provided by the first decoder.
outputs a set of values, when de-interleaved
using an converse form of the interleaver, constitute
soft estimates
of the transmitted data
sequence . This extrinsic data, formed without
the aid of parity bits from the 1st code, is feedback
This procedure is replicated in a iterative
manner. The decoding process adds greatly to the
BER performance of turbo codes. However, after
many iterations, the two decoders‘ estimates of k d
will tend to meet. At this point,
outputs a
B.
Turbo Encoding
A Turbo encoded frame is processed by multiple
convolutional encoders and interleavers, hence the
principle of these sub-blocks are presented in the
following, to support the understanding of a
complete Turbo Coding system. The encoder is a
parallel concatenated convolutional code. The
binary input data sequence is represented by , the
input sequence is passed into the input of a
convolutional encoder [7],
and a coded bit
stream,
is generated. The data sequence is then
interleaved. So, the bits are loaded into a matrix and
read out in a way so as to spread the positions of the
input bits. The bits are usually read out in a pseudo
random manner. The interleaved data sequence is
proceed to a second convolutional encoder
,
and a second coded bit stream
, is generated.
and
are Recursive Systematic
Convolutional (RSC) codes that is, convolutional
codes which use feedback (they are „recursive‟ )
and in which the uncoded data bits appear in the
transmitted code bit sequence. The code sequence
that is passed to the modulator for transmission is a
multiplexed stream consisting of systematic code
bits
and parity bits from both the first encoder
and the second encoder .
A.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 6- May 2015
value Λ( ); a log-likelihood representation of the
estimate of Λ( ) This log likelihood value takes
into account the probability of a transmitted ‗0‘ or
‗1‘ based on systematic information and parity
information from both component codes. Many
negative values of Λ(
) represent a strong
likelihood that the transmitted bit was a ‗0‘ and
more positive values represent a strong likelihood
that a ‗1‘ was transmitted. Λ( ) is de-interleaved
so that its sequence coincides with that of the
systematic and first parity streams. Then a simple
threshold operation is performed on the result, to
produce hard decision estimates, Λ( ), for the
transmitted bits. The decoding estimates
and
do not necessarily converge to a correct bit
decision. If a set of corrupted code bits form a pair
of error sequences that neither of the decoders is
able to correct, then
and
may either
concide or deviate to an incorrect soft value.
Fig3: Turbo Coded OFDM System
IV. SIMULATION
A. Simulation Algorithm
The performance of the turbo coded OFDM has
been measured through MATLAB simulation. The
simulation follows the procedure given below:
1. Generate the information bits randomly.
2. Encode the information bits using a turbo
encoder with the specified generator matrix.
3. Performed serial to parallel conversion.
4. Use BPSK, QPSK and 16 QAM modulations to
convert the binary bits ―0 and 1‖ into
complex
signals.
5. Use IFFT to generate OFDM signals, zero
padding is just done before IFFT.
6. Use parallel to serial convertor to transmit signal
serially.
7. Introduce noise to simulate channel errors. We
imagine that the signals are transmitted over an
AWGN channel.
Fig 2: Structure of Turbo Decoder
8. At the receiver side, perform reverse operations
to decode the received sequence.
III.TURBO CODED OFDM SYSTEM
9. Count the number of erroneous bits by
The merging of turbo codes with the OFDM comparing the decoded bit sequence with the
transmission is so called Turbo Coded OFDM (TC- original one.
OFDM) can yield significant improvements in 10. Calculate the BER and plot it.
terms of lower energy needed to transmit data, a
very improvement issue in personal communication
devices [5, 6].
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 6- May 2015
Fig 4: BER vs SNR plot for Turbo Coded OFDM using QPSK
B. Simulation Parameters
In this section we consider the case of the higher
order modulation techniques of QPSK. We follow
an approach of turbo coded OFDM. That is, we
Error Correcting Code
Turbo Codes
begin with the values for each subcarrier. As with
Digital Modulation
BPSK, QPSK, 16-QAM, 64- the base paper case, bp is evaluated many times,
QAM
only this time Equation is evaluated instead. We
plot the results for QPSK in Fig4. Again, Figure
Channel
AWGN
appears to demonstrate the same sort of results we
found with QPSK, and this is predictable as the
Code Generator
{111, 101}
only thing changing is based on the difference
between BPSK and 16-QAM performance in
Inter-leaver
Pseudo Random Inter-leaver
AWGN. So the task at hand now becomes simply a
matter of plotting the results of 16-QAM we
Table 1 shows the various simulation parameters drastically decrease the range of Bit error rate.
used in MATLAB. During the simulations, in order
to compare the results, some random messages B. Implementation For BPSK
were generated. For that radiant function is in
MATLAB.
TABLE I
V. SIMULATION RESULTS
First the development of an OFDM system model
then try to improve the performance by applying
forward error correcting codes to the uncoded
system. From the study of the system, it can be
concluded that we are able to improve the
performance of uncoded OFDM by convolutional
coding scheme.
A. Implementation For QPSK
Fig 5: BER vs SNR plot for Turbo Coded OFDM using BPSK
In Fig5. we plot the results for BPSK. For some
perspective, we plot the results of our evaluation
given by Code results against the two endpoints in
the uniform distribution. The results are interesting.
What we find is that for low values of 0 / b E N ,
the performance trend for the average of all subcarriers appears to be near the midpoint However,
as the SNR per bit increases, the average BER
decreases .The more at low SNR the average
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International Journal of Engineering Trends and Technology (IJETT) – Volume 23 Number 6- May 2015
VI. IMPROVED RESULTS
appears to closely split the difference between the
two boundaries. At high SNR, the average tends
toward the higher value.
C. Implementation For 16-QAM
TABLE 2
S.
No
Applied
Value of
X(Eb/No)
Base
Paper
Bit
Error
Rate(y)
TCOFD
M
QPSK
TCOFD
M
BPSK
TCOFD
M
16QAM
1.
3.00
0.0012
0.0047
0.0056
0.0040
2.
5.87
0.0030
0.0012
0.0015
0.0014
3.
7.5
0.0010
0.0004
0.0085
0.0001
Table 2: Comparison between BER of uncoded OFDM and TCOFDM
Table given above shows the comparison between
the bit error rates of the base paper and the different
modulation techniques. Value of the bit error rate is
less than the previous results which verifies the
improvement of the results.
Fig 6: BER vs SNR plot for Turbo Coded OFDM using 16 QAM
In this section we consider the case of the higher
order modulation techniques of 16-QAM. We
follow an approach similar to that for BPSK. That
is, we begin with the values for each subcarrier. As
with the BPSK case, bP is evaluated many times,
only this time Equation is evaluated instead. We
plot the results for 16-QAM in Fig 6. Again, Figure
appears to demonstrate the same sort of results we
found with 16-QAM, and this is predictable as the
only thing changing is based on the difference
between BPSK/QPSK and 16-QAM performance in
AWGN. So the task at hand now becomes simply a
matter of plotting the results of 16-QAM we
drastically decrease the range of Bit error rate.
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VII.
CONCLUSIONS
Then we tied concepts of OFDM and turbo coding
with a modulation scheme. To solve this problem
we have proposed a method to use the stronger
convolutional turbo codes. We focused our
attention to improve the performance of OFDM by
eliminating its shortcomings. It is shown that bit
error rate (BER) is improved by adding
convolutional turbo codes with OFDM and by using
different modulation schemes than uncoded OFDM.
By the study, we jump to the conclusion that better
performance of un-coded OFDM is achieved by
using convolutional turbo codes. On the other hand,
Turbo codes can eliminate the residual inter symbol
interference ( ISI ) and inter channel interference
( ICI ) and therefore reduce the length of the
required Cyclic prefix in an OFDM system. This
decreases the overhead combined with the Cyclic
Prefix. The Turbo codes which are used in OFDM
system for high data rate transmission in wireless
LANs, results in a extenisve improvement in terms
of bit error rate performance and bandwidth
efficiency.
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To conclude, this major project gives the detail
knowledge of a current key issue in the field of
communications named Orthogonal Frequency
Division Multiplexing (OFDM). We have
investigated the orthogonal frequency division
multiplexing used for high data rate transmission. A
wide investigation showed that the OFDM
inherently suffers from high Inter symbol
interference (ISI) (caused by a dispersive channel),
Inter channel interference (ICI) (caused by
frequency offset). In order to combat ISI and ICI
we have proposed a cyclic prefix and guard time
insertion between OFDM symbols, in which the
length of the guard time is made longer than the
length of delay spread. We have also investigated
that in 48 OFDM bit errors occur in burst form
rather than independent, and burst errors
extensively degrade the performance of the system.
Then we tied concepts of OFDM and
turbo coding with a modulation scheme. To solve
this problem we have proposed a method to use the
stronger convolutional turbo codes. We focused our
attention to improve the performance of OFDM by
eliminating its shortcomings. It is shown that bit
error rate (BER) is improved by adding
convolutional turbo codes with OFDM and by using
different modulation schemes than uncoded OFDM.
By the study, we jump to the conclusion that better
performance of un-coded OFDM is achieved by
using convolutional turbo codes. On the other hand,
Turbo codes can eliminate the residual inter symbol
interference ( ISI ) and inter channel interference (
ICI ) and therefore reduce the length of the required
Cyclic prefix in an OFDM system. This decreases
the overhead associated with the Cyclic Prefix. The
use of Turbo codes in OFDM system for high data
rate transmission in wireless LANs, results in a
considerable improvement in terms of bit error rate
performance and bandwidth efficiency.
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FUTURE WORK
The following are the some of the interesting
extensions of the present work:
An interesting topic for future research is to
perform more extensive performance comparisons
between FFT based BTC – OFDM and DCT based
BTC – OFDM systems under additional real-world
channel impairments, such as multipath fading,
time dispersion which leads to inter symbol
interference ( ISI ).
ACKNOWLEDGEMENT
The authors also would like to thank the
anonymous reviewers for their comments and
suggestions to improve this paper. The authors also
would like to thanks Mr. Manoj Sindhwani.
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