International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
To Improve Performance of OFDM System
Using Optimize Adaptive Coding Technique
with Convolutional and BCH Coding
Irfan Y. Khan1, Sunil U. Nyati2 and Umesh S. Bhadade3
1,2,3
Department of Electronics and Telecommunication, SSBT’s COET Bambhori Jalgaon
Abstract
In this paper Bit Error Rate performance of OFDM, 4-PSK, 16-QAM, 64-QAM System over AWGN channel is analyzed by using
convolutional coding and BCH coding. In this paper, we have considered only adaptive modulation. First we have investigated the
OFDM system performance of uncoded adaptive modulation using quadrature amplitude modulation (QAM) and phase shift
keying (PSK). To further enhance the system, we employ convolutional coding to OFDM system. In OFDM system, the Signal to
noise ratio is estimated at receiver and then transmitted to the transmitter through feedback channel, the transmitter according to
the estimated SNR select appropriate modulation scheme and coding rate which maintain constant bit error rate lower than the
requested BER. The obtained results show that a significant improvements in terms of bit error rate (BER) and throughput can be
achieved demonstrating the Superiority of the adaptive modulation schemes compared to fixed transmission schemes.
Keywords: Adaptive Modulation, Orthogonal frequency division multiplexing, AWGN channel coding, Bit error rate,
throughput, Convolutional coding, BCH coding
1. INTRODUCTION
Orthogonal frequency-division multiplexing (OFDM) is a method of encoding digital data on multiple carrier
frequencies. OFDM is used in digital television and audio broadcasting, DSL Internet access, wireless networks, power
line networks, and 4G mobile communications. OFDM is essentially identical to coded OFDM (COFDM) and discrete
multi-tone modulation (DMT), and is a frequency-division multiplexing (FDM) scheme used as a digital multi-carrier
modulation method. A large number of closely spaced orthogonal sub-carrier signals are used to carry data on several
parallel data streams or channels. Each sub-carrier is modulated with a Quadrature amplitude modulation or phase-shift
keying at a low symbol rate, and also maintaining total data rates similar to conventional single-carrier modulation
schemes in the same bandwidth.
In an OFDM transmission system, each subcarrier is attenuated individually under the frequency-selective and fast fading
channel. The channel performance may be highly fluctuating across the subcarriers and varies from symbol to symbol, In
case of frequency selective fading the increasing average signal to noise ratio with error probability decreases very slowly.
This problem can be mitigated if different modulation schemes are employed for the individual OFDM subcarriers. This
will substantially improve the performance and data throughput of an OFDM system. However the potential loss of
throughput due to seclusion of faded subcarriers can be mitigated by employing higher order modulation modes on the
subcarriers exhibiting high SNR values [2].
The primary advantage of OFDM over single-carrier schemes is its ability to cope with severe channel conditions (for
example, attenuation of high frequencies in a long copper wire, narrowband interference and frequency-selective fading
due to multipath) without complex equalization filters. The low symbol rate makes the use of a guard interval between
symbols affordable, making it possible to eliminate inter symbol interference (ISI) and utilize echoes and time-spreading
(on analogue TV these are visible as ghosting and blurring, respectively) to achieve a diversity gain, i.e. a signal-to-noise
ratio improvement.
2. SYSTEM MODELS
In this paper, sub band adaptive transmission schemes are employed to reduce the complexity. In this simulation the
instantaneous SNR of the subcarrier is measured at the receiver. The channels quality varies across the different
subcarriers for frequency selective channels. The received signal at any subcarrier can be expressed as [1].
=
+
(1)
Where the channel coefficient at any subcarrier is,
So the instantaneous SNR can be calculated using
SN
=
Volume 3, Issue 5, May 2014
is the transmitted symbol and
is the Gaussian noise sample.
(2)
Page 346
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
The conservative approach in threshold based adaptation is by using the lowest quality subcarrier. It means that the
lowest value of SNR will be used in mode selection. By using this method, the overall BER in one sub band is normally
lower than the BER target. Figure (1) shows the adaptation procedure.
Figure 1 Adaptation Procedure [2]
3. BLOCK DIAGRAM OF OFDM SYSTEM
The block diagram of this system is shown in Figure 2. The channel estimation and mode selection are done at the
receiver side and the information is sent to the transmitter using a feedback channel. In this model the adaptation is done
frame by frame. The channel estimator is used to estimate the instantaneous SNR of the received signal. Based on the
instantaneous SNR calculated, the best mode will be chosen for the next transmission frame. This task is done by the
mode selector block. At the transmitter the adaptive modulator block consists of different modulators which are used to
provide different modulation modes. The switching between these modulators will depend on the instantaneous SNR.
This block diagram is used to describe two types of adaptive modulation schemes which is based on MQAM and MPSK
scheme. The goal of adaptive modulation is to choose the appropriate modulation mode for transmission in each sub
band, given the local SNR, in order to achieve good trade-off between spectral efficiency and overall BER [2].
Figure 2 Block diagram of OFDM system[2]
4. SIMULATION RESULT
4.1 Convolutional Coding with AWGN PSK
Figure (3) shows the BER performance of QPSK over AWGN channel with convolutional coding. BPSK requires 3 dB
less of signal to noise ratio than QPSK to achieve the same BER. This outcome will hold true only if we consider BER in
terms of SNR per carrier. The results are -3displayed in figure (3). In AWGN channel, the signal is more prone to errors
and for example, at a bit error rate of 10 we need 20 dB more of signal to noise ratio than for AWGN to achieve the
same bit error rate. The probability of error is identical for BPSK and QPSK because the BER has been measured in terms
of signal to noise ratio per bit.
Figure 3 QPSK over AWGN channel with convolutional coding
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
4.2 Convolutional Coding with AWGN QAM
Figure (4) shows the 64-QAM over AWGN Channel with convolutional coding. It represent result of the signal to noise
ratio verses Bit error rate performance. In 64-QAM over AWGN Channel with convolutional coding of signal to noise
ratio is increases the bit error rate is decreases. Fig (5) shows 16-QAM over AWGN channel with Convolutional coding.
Figure 4 64-QAM over AWGN Channel with
Convolutional coding.
Figure 5 16-QAM over AWGN Channel with
convolutional coding.
Figure (6) Show M-ary PSK & QAM over AWGN Channel with convolutional coding. This figure shows the
combination of M-ary 4-PSK, M-ary 16-QAM and M-ary 64-QAM.
Figure 6 M-ary PSK & QAM over AWGN Channel with convolutional coding.
4.3 Convolutional Adaptive Coding with AWGN Channels
Figure (7) shows the result of PSK & QAM adaptive modulation over AWGN Channel with Convolutional coding. In
PSK and QAM signal are adaptive by using adaptive modulation and display the result as shown in figure (7).
Figure 7 PSK & QAM adaptive modulation over AWGN Channel.
4.4 Convolutional Coding Result
The PSK and QAM signal are adaptive by using adaptive modulation and gives this result are as shown in figure (8).
This figure indicates the Adaptive combination of Adaptive PSK and QAM. In an OFDM system using lower order
modulators such as BPSK, 4-QAM and 8-QAM will improve BER but decreases spectral efficiency, on the other hand
employing higher order modulators such as 64 QAM, 128 QAM, 256QAM and 512 QAM will increase spectral
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
efficiency but result in poor BER. So to achieve good trade-off between spectral efficiency and overall BER adaptive
modulation is used. Figure (8) shows the simulated BER performance of M-ary PSK, 16-QAM, 64-QAM and adaptive
modulation scheme for an OFDM system over AWGN channel.
Figure 8 Adaptive PSK & QAM over AWGN Channel with Convolutional coding.
The BER performance of adaptive modulation has a ripple phenomenon. This is because the BER of adaptive modulation
scheme will rise up when the constellation is switched to one with larger constellation size as SNR increases and falls in
the next SNR region. The BER will decrease when the SNR keeps increasing within the region boundaries. As the value
of SNR increases, the BER decreases and the system will switch to the next higher modulation scheme and again the BER
increases but it decreases for the chosen modulation scheme interval. But the adaptive modulation scheme keeps the value
of the BER below the target BER.
4.5 BCH Coding with AWGN PSK
Figure (9) shows the BER performance of QPSK over AWGN channel with BCH coding. In this figure the SNR increase
the Bit error rate is decreases. BPSK requires 3 dB less of signal to noise ratio than QPSK to achieve the same BER. This
outcome will hold true only if we consider BER in terms of SNR per carrier. The effects of AWGN channel simulated.
The results
-3 are displayed in figure. In AWGN channel, the signal is more prone to errors and for example, at a bit error
rate of 10 we need 20 dB more of signal to noise ratio than for AWGN to achieve the same bit error rate. The probability
of error is identical for BPSK and QPSK because the BER has been measured in terms of signal to noise ratio per bit.
Figure 9 QPSK over AWGN Channel with BCH coding
4.6 BCH Coding with AWGN QAM
Figure (10) shows the 16-QAM over AWGN Channel with BCH coding. It represent result of the signal to noise ratio
verses Bit error rate performance. In 16-QAM over AWGN Channel with BCH coding of signal to noise ratio is increases
the bit error rate is decreases. Figure (11) shows 64-QAM over AWGN channel with BCH coding.
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
Figure 10 16-QAM over AWGN Channel BCH coding
ISSN 2319 - 4847
Figure 11 64-QAM over AWGN Channel with BCH coding
Figure (12) Show M-ary PSK & QAM over AWGN Channel with BCH coding. This figure shows the combination of Mary 4-PSK, M-ary 32-QAM and M-ary 64-QAM.
Figure 12 M-ary PSK & QAM over AWGN Channel with BCH Coding
4.7 BCH Adaptive Coding with AWGN Channels
Figure (13) shows the result of PSK & QAM adaptive modulation over AWGN Channel with BCH coding. In PSK and
QAM signal are adaptive by using adaptive modulation and display the result as shown in figure (13).
Figure 13 PSK & QAM adaptive modulation over AWGN Channel with BCH Coding
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
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4.8 BCH Coding Result
Figure (14) shows the result of Adaptive PSK & QAM over AWGN Channel with BCH Coding. In PSK and QAM signal
are adaptive by using adaptive modulation and gives this result are as shown in figure (14). This figure indicates the
Adaptive combination of Adaptive PSK and QAM.
Figure 14 Adaptive PSK & QAM over AWGN Channel with BCH Coding.
5. CONCLUSION
The detail knowledge of a current key issue in the field of communications named Orthogonal Frequency Division
Multiplexing (OFDM). We elaborated on the performance theory of the codes. First we developed an OFDM system
model then try to improve the performance by applying forward error correcting codes to our uncoded system. From the
study of the system, it can be concluded that we are able to improve the performance of uncoded OFDM by convolution
coding scheme. In this paper, the performances of adaptive transmission scheme for OFDM have been investigated. The
advantage of employing adaptive transmission scheme is described by comparing their performance with fixed
transmission system. A better adaptation algorithm is used to improve the throughput performance. This algorithm
utilizes the average value of the instantaneous SNR of the subcarriers in the sub band as the switching parameter. The
results show an improved throughput performance with considerable BER performance.
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AUTHOR
Irfan Khan Yousuf Khan received his BE (E&C) Degree from G. H .Raisoni College of Engineering and
management Jalgaon, North Maharashtra University in 2011. He is a final year student of M.E in Digital
Electronics (E&Tc), SSBT’s COET Bambhori Jalgaon Maharashtra, India. He published two papers in
international journals and also published two papers in international conference.
Sunil U. Nyati received his BE ( E&Tc) degree from Govt. Engineering College, Aurangabad in 1996. He
obtained his ME in E&Tc degree from Govt. COE, A’bd.. He has 12 years of teaching experience. He
worked as a Associate Professor in E&Tc Department at SSBT’s COET bambhori Jalgaon. He has published
4 papers in international and national journals and also published around 14 papers in international and
national conferences conducted in india.
Umesh S. Bhadade received his BE ( E&Tc) degree from COET Jalgaon in 1993. He obtained his ME in
degree from COE Badnera.He is awarded Ph.D in data compression from M.S.University waroda. He has
20 years of teaching experience. He worked as a Professor and Head in E&Tc Department at SSBT’s COET
bambhori Jalgaon. He has published 07 papers in international and national journals and also published
around 21 papers in international and national conferences.
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