
Research Article


Can we Reduce PAPR? OFDM+PTS+SLM+STEGO: A Novel Approach 

Padmapriya Praveenkumar,
Rengarajan Amirtharajan,
K. Thenmozhi
and
John Bosco Balaguru Rayappan



ABSTRACT

With the rabid development of the wireless technology, the gadgets that were
once a luxury, have now become a necessity. The wireless technology was first
developed for army utilisation. But later, because of the wide applications
it had, it became a global obligation. But, today, with the advent of internet,
the security of the wireless networks has been at a great stake. And also, the
new generation of wireless network demands higher data requirements which is
achieved by higher data bits per unit bandwidth because of a variety of multimedia
applications, cost efficiency, spectral efficiency and security. In this study
all of these challenging requirements are met by Orthogonal Frequency Division
Multiplexing (OFDM) along with steganography. It is an attractive and explorative
area for the next and future generation of wireless multimedia applications.
The major challenging issue in the design of an OFDM transceiver is its high
PeaktoAverage Power Ratio (PAPR). In this study, design of an OFDM system
adopting Partial Transmit Sequence (PTS) and Selected Mapping (SLM) techniques
were concentrated. We analyse the system performance for various values of subcarriers
and modulation schemes by computing Complementary Cumulative Distribution Function
(CCDF) and analysing the Bit Error Rate (BER). PAPR value is low as the number
of subcarriers has been increased. PTS outperforms SLM when complexity is considered
and SLM is preferred when the redundant bits in the information are high and
data embedding algorithm is included after modulation to ensure wireless security.





Received: June 21, 2012;
Accepted: August 29, 2012;
Published: October 06, 2012


INTRODUCTION
Communication playing the key role in daily lives has created revolutions since
the Stone Age. Right from the stone carvings of the rock era to the early paper
usages and to the desktops till tablet PC’s. Technology develops for our
good and gets better for our best. Communication enhances at every walk, say
from mere communication to the digital communication to video conversation to
even live talks! All this happens just as a product of high data rate offered
by orthogonal frequency division multiplexing (OFDM) (Amirtharajan
et al., 2010; Praveenkumar et al., 2012ac).
OFDM is a Multicarrier multiplexing modulation technique (Amirtharajan
et al., 2010; Praveenkumar et al., 2012ac)
which offers very good spectral efficiency (Cimini, 1985)
making it suitable for high data rate in wireless, multimedia, Very Large Scale
Integration (VLSI) technology and Digital Signal Processing (DSP) applications.
Inverse Fast Fourier Transform (IFFT) and Fast Fourier Transform (FFT) pairs
(Weinstein and Ebert, 1971; Li and
Stuber, 2006; Arioua et al., 2012) in OFDM
(Kumar et al., 2008; Li and
Cimini, 1998; Thenmozhi et al., 2011, 2012;
Kumar et al., 2011) reduces the receiver complexity
and ensures orthogonality between subcarriers to reduce the Inter Symbol Interference
(ISI) (Saltzberg, 1967; Elahmar.
et al., 2007) and Inter Carrier Interference(ICI). Robustness to
the fading environment increases its attraction towards high speed mobile environment
(Chang, 1966, 1970).
The potential limitation with OFDM is its PAPR (Latif and
Gohar, 2008). SLM and PTS are the broadly used techniques to reduce PAPR
(Wu, 2011; Liang et al.,
2010; Kasari and Dehghani, 2009). PTS provides better
performance compared to SLM when complexity is considered while the PAPR value
is decreased as the number of subcarriers are increased (Chen
and Hu, 2010; Wang et al., 2009; Latif
and Gohar, 2003). PAPR causes degradation in system efficiency and its performance
(AlKebsi, 2008). In PTS, the time domain input from
the modulator is divided into smaller blocks, each multiplied by a constant
phase value and then the value with the lowest PAPR is selected and given to
the IFFT block (Sichao and Dongfeng, 2005; Wang
and Tellambura, 2006; Han and Lee, 2004).
High PAPR results in out of band and inband radiations and distortions and
affects the BER of the system (Nguyen and Lampe, 2008;
Gao and Xie, 2009). Side information is given to the
receiver in PTS technique to inform about the phase optimization value. SLM
is a probability based method where the input data has been rotated by a sequence
set of phase vector values (Saltzberg, 1967; Singhal
et al., 2009; Badran and ElHelw, 2011).
PAPR reduction in OFDM has been attempted till date for PAPR reduction in OFDM
using Partial Transmit Sequence (PTS) algorithm(Sichao and
Dongfeng, 2005; Wang and Tellambura, 2006) and Selective
Mapping algorithm (SLM) (Singhal et al., 2009;
Badran and ElHelw, 2011).
Steganography literally covered/secret writing that obscure the hidden data
in digital media (Cheddad et al., 2010; Amirtharajan
and Rayappan, 2012ad; Zanganeh
and Ibrahim, 2011). For concealing and revealing the information, a stego
key will be used (Stefan and Fabin, 2000; Amirtharajan
and Rayappan, 2012ad; Thanikaiselvan
et al., 2011a, b; Janakiraman
et al., 2012a). Cryptography, Steganography and Watermarking are
multifarious in secret data communication (Schneier, 2007;
Zaidan et al., 2010). While Cryptography scrambles
the message, Steganography conceals the existence and watermarking provides
authorization (Amirtharajan and Rayappan, 2012ad).
For sharing and transferring secret data over communication channel, either
two or three of the above mentioned techniques can be combined to provide security
at a higher level and reduces intrusion (Janakiraman et
al., 2012a, b). Amirtharajan
and Rayappan (2012c) describes the data that can be embedded on a cover
file which has to first identify the redundant bits, then embed the data without
making explicit alteration to the cover file. However, while embedding the data
on the cover, there always exists trade off between secret data extraction,
capacity, robustness and security. A various review on data embedding methods
in different domain have been analysed in Amirtharajan et
al. (2012), Janakiraman et al. (2012b),
Rajagopalan et al. (2012) and Thenmozhi
et al. (2012).
But in literature, no attempt has made till date, to make comparison between
SLM and PTS algorithms considering various subcarriers and different modulation
schemes to reduce PAPR in OFDM incorporating data embedding after the modulator
using the phase value as a key to maintain confidentiality and wireless security.
MATERIALS AND METHODS
Block description of OFDM system
Modulation: In an OFDM system as shown in Fig. 1, the
serial input data as in Fig. 2 is converted to a parallel
form and the bits are grouped based on the modulation scheme adopted. The mapping
employed can be Quadrature Amplitude Modulation (QAM), Binary Phase Shift Keying
(BPSK) or Quadrature Phase Shift Keying (QPSK). The number of bits per symbol
for BPSK is 1 bit, for QPSK is 2 bits and for QAM it is 3 bits.
The modulated and demodulated outputs are shown in Fig. 3
which indicates that the data has been demodulated correctly.
Inverse fast Fourier transform (IFFT): The modulated data is sent to
IFFT which maps the frequency domain signal into the corresponding time domain
signal. The output signal from IFFT will introduce Peak to Average Power Ratio
(PAPR). Hence an Optimization technique to reduce PAPR has to be carried out.
The IFFT output is shown in Fig. 4. It is mainly used in OFDM
to provide orthogonal subcarriers to ensure high spectral efficiency.

Fig. 1: 
Schematic diagram of the proposed OFDM system 

Fig. 2: 
Input bit stream to the OFDM system 

Fig. 3: 
Modulation and Demodulation of the input bit stream using
BPSK 

Fig. 4(ab): 
(a) IFFT and (b) FFT outputs 

Fig. 5(ab): 
Cyclic (a) Prefix and (b) Deprefix outputs 

Fig. 6(ab): 
(a) AWGN channel and (b) A/D conversion outputs 
Cyclic prefix (CP): The CP is appended to the OFDM system by adding
a part of the end symbol of the OFDM signal to the original signal. This reduces
the Inter Symbol Interference (ISI) and improves the BER of the OFDM system.
The CP and the cyclic deprefix outputs are shown in Fig. 5.
Digital to analog conversion (D/A): The output digital data from the
cyclic prefix should be converted into analog form before passing onto the channel.
Additive white Gaussian channel (AWGN): AWGN channel introduces additive
white noise into the signal that passes through it. The signal output after
passing on to the AWGN channel is shown in Fig. 6.
Analog to digital conversion (A/D): For data recovery at the receiver,
the analog data output from the AWGN channel should be converted back to digital
form. It includes sampling and quantization. The result is shown in Fig.
6.
Cyclic deprefix: The reverse process of the cyclic prefix is implemented
in the receiver. The data bits added to the OFDM symbol are removed to get back
the original data. The implementation is shown in Fig. 5.
Fast Fourier transform (FFT): The time domain data is converted into
frequency domain by the FFT process. For the demodulation to take place the
data should be in frequency domain. The result of FFT is shown in Fig.
4.
Demodulation: The data in the frequency domain is demodulated at the
receiver to recover the original input. The original bits sent at the transmitter
end were recovered at the receiver end and is shown in Fig. 3.
Data embedding after modulator: The secret data has been embedded after
modulator using the phase value as the key. Generally modulator output is given
by:
φ represents the phase angle, E_{s} represents signal energy,
T represents symbol duration and φ value varies based on the modulation
techniques adopted. The in phase and Quadrature component values before and
after embedding 0 and 1 when bit 0 and 1 was transmitted using BPSK modulation
respectively is tabulated in Table 1.
The in phase and Quadrature component values before and after embedding 0 and
1 when bit 0 and 1 was transmitted using QPSK modulation respectively is tabulated
in Table 2.
PAPRSLM and PTS algorithms: In OFDM, the large number of independent
subcarriers when added result in high PAPR. It is the ratio of Peak power/average
power:
where, Maximum represents the OFDM signal’s
peak power and E represents the expected value. a_{k }represents the
data symbol with k=0 to k1.
Table 1: 
BPSK constellation table 

Table 2: 
QPSK constellation table 


Fig. 7: 
Block diagram for PTS algorithm 
The modulated output in frequency domain is represented in complex form and
is given as:
where, 0 = t = lT where l denotes the subcarrier and Δ_{f} represents
subcarrier spacing.
CCDF is the performance measure to analyse PAPR. CCDF indicates that the probability
(PAPR_{data}) should exceed threshold. CCDF =P(PAPR > Z) = 1 P(PAPR
= Z).
Steps in PTS algorithm:
• 
Divide the OFDM symbol into set of independent sub blocks
as in Fig. 7 
• 
PAPR reduction can be achieved by rotating the subcarriers in each by
some set of phase sequences 
• 
The frequency domain output of the signal mapper is denoted as A and is
partitioned in to ‘i*’ set of sub blocks and is denoted as a^{i*}
where i*=1, 2…….I 
• 
A is the frequency domain output 
• 

• 
A^{i} are independently phase rotated by 
• 
c_{i} = ce^{jφi} 
• 
So 
• 
Then after passing onto the IFFT, it is given by y' = IFFT (A^{I}) 
If i1 are the sub blocks and c is the phase vector, then c^{i1 }values
have to be verified to provide minimum PAPR.

Fig. 8: 
Block diagram of SLM algorithm 
Steps in SLM algorithm:
• 
The input data is divided into many number of different sets
as in Fig. 8, holding the same original information and
are converted to parallel form 
• 
They are then subjected to phase vector multiplication. The phase vector
is represented by B_{v }and v=0, 1…v1 
• 
V different phase vector values are selected, in which each set contains
and N be the number of elements in the original information B_{v}
= [B_{v0}, B_{v1}.... B_{vN1}] 
• 
The phase vectors are selected to provide optimized PAPR 
• 
Normally the phase vectors can be selected from the set {±i, ±1} 
• 
After performing multiplication, the modified set will be B = [B_{0},
B_{1}.... B_{u1}] 
They are then passed through IFFT and the PAPR value is computed. The set with
minimum value of PAPR is chosen and transmitted to D/A converter.
RESULTS AND DISCUSSION
Figure 9 shows the performance comparison of SLM and PTS
using BPSK modulation for subcarrier values of 2, 4 and 8 in terms of Complementary
Cumulative Distribution Function (CCDF) (Nguyen and Lampe,
2008; Gao and Xie, 2009) and Bit Error Rate (BER).
The performance comparison of SLM and PTS using QPSK modulation for subcarrier
values of 2, 4 and 8 in terms of Complementary Cumulative Distribution Function
(CCDF) and Bit error rate (BER) is given in Fig. 10.

Fig. 9(ab): 
CCDF plot for various subcarriers of 2, 4 and 8 using BPSK
modulation for (a) SLM and (b) PTS 

Fig. 10(ab): 
CCDF plot for various subcarriers of 2, 4 and 8 using QPSK
modulation for (a) SLM and (b) PTS 
Figure 11 shows the performance comparison of SLM and PTS
using QAM modulation for subcarrier values of 2, 4 and 8 in terms of complementary
cumulative distribution function (CCDF)) and Bit error rate (BER)
From Fig. 911 it is noted that the PAPR
reduction is at its best for U = 8 and comparatively low for U = 2 (Singhal
et al., 2009; Badran and ElHelw, 2011).
When the number of phase vectors is increased, a better reduction of PAPR can
be achieved as discussed in (Chen and Hu, 2010; Wang
et al., 2009; Latif and Gohar, 2003). The
phase vectors U = 2, 4 and 8 are used for SLM while V = 2, 4, 8 are considered
for PTS. It is seen that the PAPR decreases as the number of U and V values
are increased in SLM and PTS respectively as mentioned in (Wu,
2011; Liang et al., 2010; Kasari
and Dehghani, 2009). From the results obtained, QAM provides better PAPR
reduction compared to QPSK and BPSK. Similarly PTS with U = 8 provides better
PAPR reduction.
Figure 12 shows the BER comparison between SLM and PTS techniques
for the subcarrier value of 8.

Fig. 11(ab): 
CCDF plot for various subcarriers of 2, 4 and 8 using QAM
modulation for (a) SLM and (b) PTS 

Fig. 12(ab): 
Comparison between BPSK, QPSK and QAM for 8 subcarriers for
(a) SLM and (b) PTS 
The BER comparison between SLM and PTS techniques for the subcarrier value
of 4 is given in Fig. 13.
Figure 14 shows the BER comparison between SLM and PTS techniques
for the subcarrier value of 2.
The BER comparison between SLM and PTS techniques using QPSK for the subcarrier
value of 8 and BPSK for the subcarrier value of 2 is given Fig.
15.
The comparison between SLM and PTS using QAM is plotted in Fig.
16.
From the results obtained through the plots 12 to 16, BPSK with U = 8 for PTS
outperforms than the other two modulation schemes. As the phase vector values
are increased, better reduction in PAPR is achieved for both SLM and PTS algorithms.
In PTS, all the elements present in the sub blocks are being multiplied by
the equal phase values. In PTS, the phase rotation vectors are limited to specific
values.

Fig. 13(ab): 
Comparison between BPSK, QPSK and QAM for 4 subcarriers for
(a) SLM and (b) PTS 

Fig. 14(ab): 
Comparison between BPSK, QPSK and QAM for 2 subcarriers for
(a) SLM and (b) PTS 
In the case of SLM, data sequences are required to be rotated with different
phase sequences_{. }PTS provides comparatively superior reduction in
Peak values. In PTS, it is clear that the PAPR decreases obviously as the number
of subblocks get increased with respect to primary OFDM. When there are redundant
bits in OFDM symbol, SLM can be considered. PTS is more suitable when design
complexity is considered.

Fig. 15(ab): 
Comparison between SLM and PTS using (a) QPSK modulation
for 8 subcarriers and (b) BPSK modulation for 2 subcarriers 

Fig. 16: 
Comparison between SLM and PTS using QAM 
Both the techniques scramble the serial data input symbols and the one with
the lowest peak average power ratio is transmitted to reduce the probability
of high Peak Average Power (PAP).
In SLM, phase sequences used are in a pseudorandom fashion, so no knowledge
about the phase values at the transmitter end which increases the receiver complexity,
are known. In PTS, the phase vector set consists of only few values which are
known at the transmitter end reducing the complexity of designing the receiver.
SLM requires more number of phase vector set to select the optimized one with
least PAPR while in PTS, an efficient phase vector set is sufficient for reducing
the PAP.
Comparing all three modulation schemes, PTS outperforms SLM. Design complexity
in the transmission and reception part decreases the rate of the data to provide
distortionless system in SLM.

Fig. 17: 
Constellation diagram of BPSK before and after data embedding 

Fig. 18: 
Constellation diagram of QPSK before and after data embedding 
Complexity is an important consideration as far as the OFDM system is considered.
So PTS could be a better candidate in reducing PAPR. When both PAPR reduction
and redundancy are taken into account, both the schemes provide almost the same
results. SLM is more appropriate for the system that can withstand redundancy
in the information. In both the techniques, phase vectors are added along with
the information which helps in data recovery.
Constellation diagram before and after embedding 0 when input bit is 1 and
0 and before and after embedding when input bit is 1 and 0 using BPSK modulation
is shown in Fig. 17.
In addition, Fig. 18 shows the constellation diagram before
and after embedding 0 when input bit is 1 and 0 and before and after embedding
when input bit is 1 and 0 using QPSK modulation.
From Fig. 17 and 18, it is clear that
the secret data can be extracted before demodulator by knowing the in phase
and quadrature phase components, the bit transmitted and the bit embedded during
transmission otherwise secret will be maintained as secret itself.
CONCLUSION
OFDM is a multicarrier modulation scheme and is preferred for highspeed data
transmission over fading channels. It has various merits but also has one major
stumbling block of very high PAPR. In this paper, the different characteristics
of OFDM System are analyzed. The graphs are plotted using CCDF against BER for
different modulation schemes employing different subcarriers. We employed PAPR
reduction techniques such as PTS and SLM which reduces the PAPR of the proposed
system. Among the two techniques it is found that both the PTS and SLM techniques
provide considerable reduction of PAPR without any loss of input data. From
the comparison of the SLM and PTS techniques, it is inferred that PTS is more
efficient in PAPR reduction. However, SLM is appropriate when there exists redundant
data bits. In terms of complexity, PTS is the promising candidate. Also, the
number of computational steps is less in the case of PTS. For providing wireless
security and confidentiality secret data has been embedded after modulation
using the in phase and quadrature phase values of the modulator as the key.

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