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Coded Crypted Converted Hiding (C3H)-A Stego Channel

Padmapriya Praveenkumar, Rengarajan Amirtharajan, K. Thenmozhi and J.B.B. Rayappan
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Wireless communication with its enhanced efficiency, greater flexibility, mobility and reduced cost has encompassed human needs and sophistications to a greater extent. Several techniques adopted in the wireless standards solely contribute to its heightened demand. Orthogonal Frequency Division Multiplexing (OFDM) is one such technique adopted to provide robust and high speed networks by countenancing signal overlap. The data transmission over wireless, in general, is unsecure and open to the hands of the hackers. A method has to be devised to uniquely identify the copyright owner of the data and prevent the misuse of it. However, on the negative side, the hackers are on par with the experts in this field to combat against piracy. This necessitates the introduction of a method to impart security to the data being transmitted. This study embeds secret data bits in punctured convolutional encoder’s output which are then encrypted using Chaotic means to uphold discretion and then traversed through OFDM system. Integrity is made certain by four-level encryption of secret bits and is stored as key in Cyclic Prefix’s (CP) output. Comparison graphs for the performance of Bit Error Rate (BER) are plotted for different modulation schemes with different rates of punctured convolutional codes. These plots are for puncture patterns with convolutional encoder before and after infixing the secret data. Widespread metrics for performance measurement like BER and correlation values are computed. This estimation reveals the security and arbitrariness of data encryption both with and without embedding the secret.

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Padmapriya Praveenkumar, Rengarajan Amirtharajan, K. Thenmozhi and J.B.B. Rayappan, 2014. Coded Crypted Converted Hiding (C3H)-A Stego Channel. Journal of Applied Sciences, 14: 1786-1797.

DOI: 10.3923/jas.2014.1786.1797

Received: December 14, 2013; Accepted: January 18, 2014; Published: April 18, 2014


The telecommunication technology needed a transition from 3rd generation to 4th generation to cater higher data rates in multimedia applications. OFDM is a unique form of multi carrier scheme (Bahai et al., 2004), though projected in 1960s, was dormant for few years. Implementing it in real time was a hard row to hoe until the semiconductor and computer technologies advanced in the recent years. Frequency Division Multiplexing (FDM) has evolved to gain omnipotence and yielded OFDM (Chang, 1966; Chang and Gibby, 1968; Chang, 1970).

OFDM is a digital modulation technique that converts the serial data stream into smaller sub-streams and modulated on low speed sub-carriers. The entire spectrum is divided into smaller sub bands which makes the channel effects flat over a sub band of concern. This makes the symbol period much longer than the delay spread produced by the time dispersive channel (Saltzberg, 1967). Longer symbols are less susceptible to multipath interference and the synchronization is made easier by the lower data rate of sub streams. The sub-streams are modulated through any of the popular modulation techniques like Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK) and M-ary Quadrature Amplitude Modulation (QAM). This study proposes a unique steganographic tactic using contemporary multiplexing know-how. OFDM is a widely accepted beneficial means for steganography as it possesses competent interference dismissal and admirable bandwidth efficacy. Moreover, unlike any other stego methods, OFDM smoothes progress of implementation. It also minimizes system cost. It lends a helping hand for steganography by resisting fading; this makes OFDM an important come reliable steganographic mode.

Transmission is achieved through any of the media like wireless, coaxial cable, fiber-optic cable, twisted pair cable. Cyclic prefix, also known as guard intervals are inserted to combat inter channel interference and inter symbol interference. All these features coupled together make OFDM superior to all other transmission techniques. Also, OFDM allows the sub carriers to overlap without causing inter-channel interference (Weinstein and Ebert, 1971; Thenmozhi et al., 2011, 2012). This is achieved by orthogonal nature of the subcarriers. Orthogonal in the sense, the peak of the carrier will occur at the null of the other.

The orthogonality is incorporated to the symbols by performing Inverse Fast Fourier Transform (IFFT) operation. IFFT and Fast Fourier Transform (FFT) serves the purpose of modulation at the transmitter and demodulation at the receiver. The basis functions of IFFT are orthogonal sinusoidal. IFFT assigns each sinusoid with a phase and amplitude depending on the values of the corresponding symbol (Van Nee and Prasad, 2000). The number of IFFT inputs determines the number of sub-carriers in OFDM. The output will be a complex sum of all the sinusoids with varying phase and amplitude. All these output samples corresponds to one OFDM symbol. In the same way, demodulation is performed by FFT operation (Hwang et al., 2009). This makes the OFDM system, especially for steganography, substantially computation efficient.

In wireless communication, error detection and error control are the methods which facilitate steadfast electronic data delivery through erratic communication medium which introduce unwanted signals, introducing errors all through transmission from one end to other. While the former permits identifying errors, the latter makes possible the restoration of original signal or message. Error control codes are of great help when it comes to secret sharing. They add one more level of security. The aim of this study is to maintain privileged secrecy level through manifold persona of a cohesive system. In Forward Error Correction (FEC), the data is encoded by Error Control Code (ECC) before transmission. It is recovered at the receiving end through redundant information added during transmission (Peterson and Weldon, 1972).

FEC finds applications where there is impossible or expensive retransmission, for instance, one-way communication linkage and transmission to more than one receiver at a time etc. FEC has two main classifications viz., block codes, convolutional codes (Forney, 1971). The former operates on fixed-size packets (blocks of bits) or symbols and the latter functions on bit streams or symbols of random length. Noteworthy here is the fact that these classifications are very obliging in constructing different steganographic algorithms. Also, they manage the computational parts of secret data bits on the word of the implementation. Convolutional encoder is described in two forms, polynomial description and trellis description (Forney, 1970). The first one has two constituents namely Constraint lengths and Generator polynomials. Constraint lengths generate a vector of length as per (number of) inputs in the encoder which indicates the total bits accumulated in shift register, together with that of the present input (Forney, 1973).

For illustration, say, it possesses k, n inputs and outputs respectively, then, k-by-n is called code generator matrix. The effect of possible encoder input on its output and state transitions is better explained by a trellis description. If some parity bits are removed after they get encoded by ECC, then it is called puncturing. This seems similar to that when done via ECC having lower redundancy or higher rate. On the other hand, with this puncturing, unchanged decoder can be utilized irrespective of the number of bits punctured, thus leading to increased flexibility devoid of appreciably escalating intricacy. On the whole OFDM remains immune against frequency-selective fading straits thereby making steganographic algorithms well-built (Praveenkumar et al., 2012a-c, 2013a-j).

The nascent field of Information Hiding has got over post-natal hiccups to evolve into a robust field of technology to combat piracy (Al-Azawi and Fadhil, 2010). Its growth has increased manifold in the past few years and a spate of effective techniques have been developed (Amirtharajan and Balaguru, 2011; Amirtharajan and Rayappan, 2012a-d; Amirtharajan et al., 2011, 2012a, b; Ramalingam et al., 2014; Thanikaiselvan et al., 2012a, b, 2013; Thenmozhi et al., 2012).

Among them, steganography and Water-marking have evoked maximum interest from scientists (Amirtharajan et al., 2013a-e; Stefan and Fabin, 2000; Janakiraman et al., 2012a, b; Padmaa et al., 2011; Rajagopalan et al., 2012). Steganography in Latin means covered writing. It involves hiding the desired message inside another message called cover (Al-Frajat et al., 2010; Alanizi et al., 2010). The prime aim in steganography is to maintain the secrecy (Anderson and Petitcolas, 1998). The hidden message can be known only to the sender and receiver. Putting it in other words we can say that this technique serves its purpose only if the presence of secret message is undetectable, failing which renders this technique useless (Tseng et al., 2002). An image is most often the preferred choice of cover. The redundancy in an image is used to camouflage the message. Spatial domain steganography, transform domain steganography and spread spectrum image steganography are some of the subcategories of Steganography. Spatial domain steganography embeds the secret information directly into the pixels of the cover object. For instance, the cover object can be an image, video or audio file. LSB (Least Significant Bit) steganography is a classic example of Spatial domain steganography (Amirtharajan and Rayappan, 2013; Petitcolas et al., 1999).

In this technique, the bits of the least significant plane are used to embed data. These bits are replaced with the secret message and the overall quality of the cover object remains unaffected. It is the simplest technique of all and has a very high payload. On the contrary, this simplest technique would lend itself to attack by the hackers and is more likely to be deciphered than other techniques. Several detection methods are already available. Transform domain steganography transforms the cover image using one of the transforms like fourier transform, Hartley transform and wavelet transform. Embedding is performed on the transformed coefficients of the cover image.

In Watermarking a piece of data is embedded into the message to be transmitted. This piece of data is called watermark and it served to uniquely identify the copyright owner for that piece of data. This technique, unlike steganography, gives much importance to robustness. Its essence lies in the inability of a hacker to remove the watermark alone without significantly damaging the data. Cryptography is a data security technique that introduces security by making the message undecipherable rather than hiding it. We prefer steganography to cryptography as it is better to make the message undetectable than to leave it in undecipherable form in the hands of the hacker, since there is a possibility for a hacker to break the encryption method by some means (Schneier, 2007). The power of steganography increases by several folds when it is incorporated with cryptography.

Spread Spectrum Image Steganography (SSIS) uses spread spectrum concept used in digital communication (Marvel et al., 1998). It is a technique that broadens the bandwidth of a message signal which usually takes the narrowband form by combining over a wide band of spectrum that ensures the energy of the narrow band message waveform is lower at any frequency. This aspect is exploited to incorporate security in an image through spread spectrum. SSIS camouflages the message inside the noise typical to the natural image (Marvel et al., 1999). This noise is combined with the cover image to yield the stego image. Thus the message is well-incorporated without changing the statistical properties of the image.

The security provided by the steganography has to be broken when used by the wrong-doers for passing messages between them. The art of detecting the presence of secret message is known as steganalysis (Amirtharajan et al., 2012c). It is the reverse procedure of steganography methodology. The residual effects left by the steganography algorithms on the image have aided the development of steganalysis methods. Most of methods use the statistical properties of the image to compare the cover image and the stego image. However these methods can only detect the presence of hidden message, but cannot be used to retrieve it. Various information hiding techniques in OFDM has been suggested by Thenmozhi et al. (2012), Lin et al. (2006) and Lin and Pan (2008). Secret sharing skill can be much more made resourceful by taking up encryption algorithms. Thus this blend guarantees security and immunity to a routine both at the steganographic and cryptographic levels.

Image cryptosystem promises enhanced security when it is combined with chaotic (block cipher) algorithm. Authentication is assured with the use of hash based encryption means. Besides these, pixel based image encryption methods are also found to be valuable. Fast encryption routines, literally minimizes the computation time. Image encryption, on the whole, sees to the fact that the secret message is austerely enciphered. Since they do not give even a hint of what is hidden, this demesne has astonished many. The fundamental motto of Chaos is to scramble image pixel positions; which is highly reliable as far as image encryption is concerned (Alvarez et al., 1999, 2000; Zhu, 2012). This kind of encryption practices two basic ideas: Mystification and dissemination. It executes image encryption via pixel scuffling and grey scale spaying. At this moment in time, chaotic means proves to be an amazing tool for image encryption due to its sharp compassion to minute changes too. Therefore, this means to be a rapid, unharmed encryption means.

After reviewing the available literature on steganography, OFDM and FEC, this study proposes a data embedding scheme after convolutional encoder utilizing puncture patterns, then four levels of encryption is done on the secret data bits and is stored as a key value at the output of CP to maintain integrity and confidentiality of the data transmitted over wireless transmission system. Comparison graphs among various modulation schemes before and after embedding the secret data bits are plotted, then encrypting the secret data bits and embedding it after CP output is also plotted. Chaotic based encryption is done on the punctured outputs and the metrics are computed to validate the performance.


Sample input data bits of 256 are passed over scrambler. It is a popular technique in digital data transmission to protect the data from burst errors. There may be cases when a lot of errors occur subsequently in a row during data transmission. Such errors correspond to burst errors and they exceed the correctable number of bits in an error correction scheme. This scenario can be overcome by interleaving the data before transmission. This scrambling ensures that many errors do not occur in the same code word and will be distributed over many code words.

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Fig. 1: Proposed block diagram

It needs a specific key or algorithm to stipulate the scrambling of the data bits as shown in Fig. 1.

Then the scrambled data’s are passed over channel coder. The most popular among them is convolutional coding and Viterbi decoding. It’s a prodigious type error-control coding, the message bits come in serially rather than in blocks and hence it should have memory. Despite the fact that the convolutional coder acknowledges fixed message symbols producing fixed code symbols, the reckoning counts on certain prior input values apart from that in progression. It consists of finite number of shift registers in the company of given connections provided to modulo-2 adders and multiplexer which serializes the former’s outputs. It accepts k input bit streams from 2k possible input symbols and produces n output bit streams out of 2n possible output symbols. The rate of the encoder is given as n/k where n represents the number of input bits and k represents the number of output bits. Then coded output bits are outputted to puncturing matrix/vector. It is the one, where the bits are deleted according to a pre-defined pattern.

Then, the inverse operation, known as de-puncturing, is implemented by the decoder. Output vector is generated by the puncture blocks which is done through eliminating certain elements in input vector but by maintaining others. Input may either be real or complex of length K.

Table 1: Simulation parameters
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Operation of puncture block depends on its parameter (Puncture Vector) which is given below. Let the parameter be k:

If k = 0, then the input vector’s kth element is eliminated
If k = 1, then the input vector’s kth element gets potted in output vector

Puncture vector parameter’s length should divide K. If needed, this process is repeated for the entire input elements. The simulation parameters used in the proposed system is given in Table 1. Then the confidential data bits are embedded at the output of the punctured convolutional encoder. Four level of encryption is done on the secret data bits and then they are stored as a key in the cyclic prefix part to maintain integrity.

Embedding algorithm to store the confidential data bits:

Calculate the length of the punctured data output bits
Divide the entire length in to different shares of equal length, decide the range of embedding
Get the number of bits to be embedded
Convert the secret bits into bipolar form
The pattern is repetitive and starts from the LSB to MSB in a reverse manner

Consider m2 to be the secret data, l be the length of m2 and share be Q. Let N be the length of the actual data output, Q1 decides the data bits to be embedded. Q1 = Q*l The length of the data after embedding Q2 = N-1, Q* = Q2 -Q1.

Encryption to store the secret key value at CP:

Get the secret data input bits, flip it horizontally and use it as the key for the process
Take XOR of actual input bits with the flipped one and the output is represented as ak Eq. 1:

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Then for the output obtained, perform bitwise XOR keeping the LSB bit unchanged, it is represented as Ck Eq. 2:

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Then keeping MSB unchanged, perform bitwise XOR to the encrypted data which is represented as dk Eq. 3:

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Then this encrypted value as a key is embedded after CP to maintain integrity between transmitter and receiver.

Decryption to retrieve the key:

At the receiver end, the region of embedding should be known, to carry out decryption process
Get the encrypted data
Retaining the MSB of the acquired data, perform XOR operation from left to right Eq. 4:

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Retaining LSB of dec1k, perform XOR operation from left to right Eq. 5:

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To get the original data, dec2(n+1)-k is XORed with the key

Extraction of the confidential information: After identifying the range where embedding has happened, extraction of the secret data can be performed using Eq. 6:

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Sk Can either be 1 or -1 and Zk Can be any processed function. Then chaos based encryption is done on the punctured data bits.

Chaotic process: Chaos can be defined as a dynamic system which relies largely on preliminary specifications that are subjective and impulsive. The input values get encrypted using shambling bit values and modifying it to produce ciphered output values. This is done by the formulae:

A (c+1) = mod(b (c) +1-1.4*A (c)* A (c), 256) +1

b (c+1) = mod(0.3*A (c)+0.7*b (c)*A (c), 256)+1

where, (A, b) is bit location, A is row; b is column. (A, b) is mapped to a different location by means of transformation function.

Chaotic algorithm:

Generate random values from 0 to 255
XOR the above values with bit values in the input by:

A (c+1) = mod (b (c)+1-1.4*A (c)*A (c), 256)+1

b (c+1) = mod (0.3*A (c)+0.7*b (c)*A (c), 256)+1

Now the values are modified. The above given equations produce recursive random numbers; also, user is allowed to provide the key to set initial values. Then the encrypted data bits are sent to the modulators like BPSK, QPSK, 8, 16, 32 and 64 QAM, respectively to produce complex constellation points.

With the sub carriers very closely placed, mutual influence is hard to avoid but higher efficiency can still be obtained by the proper selection of the orthogonal frequencies using IFFT. By Cyclic prefix, ¼ th of total OFDM symbol is added as prefix to combat ISI (Inter symbol Interference) and ICI (Inter Carrier Interference).

Then scaling is done to transmit the data over Additive White Gaussian Noise (AWGN) channel. At the receiver end, two types of decryption algorithm should be known at the receiver end. One to extract the secret data bits, other to extract the data output bits. Two keys are required at the receiver end .If K2 is known decryption of the original data bits over OFDM system will be extracted. If both the keys K1 and K2 are known, then secret data bits along with the original data are recovered contributing to the security multi-fold.


Sample input data bits of 256 are passed over OFDM system considering AWGN channel, graphical results are for BER comparison of Punctured convolutional encoder with dissimilar rates of according 1/2, 1/3, 2/3 and 3/4. It is vivid that performance is intensified as secret bits are embedded after deflated convolutional encoder after which chaotic encryption is performed.

Pixel correlation is one of the important parameters for consideration of image encryption. Every image encryption algorithm aims at reducing this correlation to literally null value to vindicate the performance. Correlation value of 1 show that high correlation exists among pixels and 0 indicates the other way round. If the result is 0, then the algorithm offers nothing but abstruseness and randomness. This work is justified in this regard where encrypted data show 0 correlation among their pixels. The correlation coefficient is given by:

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Here, σi, σj is i and j’s standard deviations accordingly, cov (i, j) is covariance of i and j.

Covariance is expressed as:

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Here, μi and μj are i and j’s mean, respectively.

From Fig. 2-9, for a BER of 10^-2, BPSK is 2.5 dB which is 1 dB improvement over QPSK which in turn provides an improvement of 3 dB with respect to 8 QAM, which provides an improvement of 3 dB with respect to 16 QAM, which overruns 32 QAM by 5 dB which is heightened by 5 dB with respect to 64 QAM.

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Fig. 2:Encrypting the data without embedding using convolutional encoder of rate 1/2

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Fig. 3:Encrypting the data without embedding using convolutional encoder of rate 1/3

The maximum improvement in SNR by 17.5 dB is guaranteed with respect to BPSK.

From Fig. 2-4, for an Eb/No of 10 dB, 64 and 32 QAM is around 10^-1, 16 QAM is around 10^-2, 10^-3 for 8 QAM, QPSK is around 10^-5 and BPSK is about 10^-6. Thus QPSK and BPSK schemes are ideal among all the modulation schemes when encryption is considered.

Figure 6-9, respectively provides BER curve before and after embedding the secret data bits adopting convolutional encoder of rates 1/2, 1/3, 2/3 and 3/4. Before and after embedding the secret data bits in 1/2 and 1/3, there is no appreciable change in BER curve before and after embedding the confidential information till 16 QAM, then after 0.1 TO 1 dB difference exists before and after embedding the secret data bits in 32 and 64 QAM.

Before and after embedding the secret data bits in 2/3 and 3/2, there is no appreciable change in BER curve before and after embedding the confidential information till 8 QAM, then after 0.1 to 1.2 dB difference exists before and after embedding the secret data bits from 16 QAM onwards in 2/3 coding.

Table 2: Encrypted data metrics with embedding
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Fig. 4:Encrypting the data without embedding using convolutional encoder of rate 2/3

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Fig. 5:Encrypting the data without embedding using convolutional encoder of rate 3/4

In 3/2, till 15db all the modulation schemes work fine and 64 QAM alone deteriorates from 0.1 to 2 dB. Thus other than 64 QAM, all other modulation schemes are ideal when embedding is concentrated.

Table 2 provides the data metrics for encryption with embedding. It is obvious that BER of the convolutional encoder of rate 2/3 is higher which denotes that it produces more random and obscure output. Those with other rates are also competently high error giving justifying this encryption. Besides this, correlation values are also brought to minimum level. This means the bonding between data bits i.e., data bits relationship is very much affected due to the encryption. So, the output shows no sign of having surreptitious information in it. The 2/3 encoder gives the best results of all encoders when encryption is considered.

Table 3 provides the encrypted data metrics for different coding rates. This study has resulted in anticipated bit error rates of approximately 0.06. Of all, one with the rate of 2/3 offers high BER which signifies that the error produced is comparatively greater. This shows how random the encrypted data is. Without qualm, this study has brought the pixel correlation to a zilch which is what most needed in an encryption routine. This is so because, the minimum the correlation, the maximum is the obscurity and in turn security. Convolutional encoder with rate 2/3 is good at this regard.

Table 3: Encrypted data metrics without embedding
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Fig. 6:Comparison among various modulation schemes using convolutional encoder of rate 1/2 with embedding before encryption

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Fig. 7: Comparison among various modulation schemes using convolutional encoder of rate 1/3 with embedding before encryption

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Fig. 8: Comparison among various modulation schemes using convolutional encoder of rate 2/3 with embedding before encryption

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Fig. 9: Comparison among various modulation schemes using convolutional encoder of rate 3/4 with embedding before encryption

Figure 10 and 11 gives the cyclic prefix output before and after embedding the key value, from the figure there is no appreciable change in the cyclic prefix output before and after embedding.

When the two methods i.e., with and without embedding are concerned, method one outperforms the second as the high correlation existed among pixels in original image is brought to a zilch after encryption. The encoders with rates 1/3 and 2/3 are able to produce correlation values of 0.0227 and 0.0157, respectively which is slightly greater than the second method.

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Fig. 10: Cyclic prefix output before embedding

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Fig. 11: Cyclic prefix output after embedding


OFDM makes the most of orthogonality that exist between carriers which is the most substantive requisite for broadband applications as they oblige privileged bandwidth. In this study, incorporated assorted rates, puncture patterns, modulation schemes and finally at the output of the convolutional encoder, secluded bits are engrafted to ensure confidentiality. Subsequently, four-level encryption is performed on the secret data bits which are then hired away as the key at cyclic prefix’s output. This serves as an integrity appraisal between the two parties indulged in communication. Hence, QPSK and BPSK are the preferred modulations as they prove to be an effectual means to share the secret encrypted data. Thus, soaring level of privacy and security is maintained all through the process and any line of attack would go in vain. Thus robustness is ensured as well. As far as the two methods are concerned, method 1 is found to be beneficial as it emerges with incredible results. Of convolutional encoders with different rates, the one with rate 2/3 is found to be superior. Graphical testimonies are illustrated for BER amongst versatile modulation schemes engaging convolutional encoders of diverse puncture patterns and rates. Wireless technology blended with information security emerged as a solution to all types of communication threats and problems. This expertise with technical advancements will definitely be one worth to explore. When the gap is bridged between some technical problems and practical implementation possibilities, future scope is very promising for the forth coming wireless generations.

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41:  Praveenkumar, P., R. Amirtharajan, K. Thenmozhi and J.B.B. Rayappan, 2012. Phase for face saving-a multicarrier stego. Procedia Eng., 30: 790-797.
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49:  Praveenkumar, P., K. Thenmozhi, S. Vivekhanandan, J.B.B. Rayappan and R. Amirtharajan, 2013. Intersect embedding on OFDM channel-a stego perspective. Proceedings of the IEEE Conference on Information and Communication Technologies, April 11-12, 2013, JeJu Island, pp: 1211-1214.

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