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Transmission Of Encrypted Image Over Ofdm

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Abstract:

Wireless communication refers to transfer of data and information without using a wire, cable or any electrical conductor. It is one of the most relevant technologies in mobile and computer communication today, primarily making use of radio waves. With emergence of this technology, security of the data transmitted became a pertinent issue. To prevent unauthorised access to valuable information, various security standards have been developed over the past few years. These improvements include methods such as encryption of data at source and decryption at destination, scrambling of data, precoding etc. In this paper, a dual sided encryption and scrambling technique for an OFDM system is proposed. An XOR algorithm is employed for encryption and chaotic scrambling is used to fulfil scrambling needs. The method is employed for a channel which is characterised by AWGN. 16-QAM is utilised to modulate the encrypted data. The BER for various images is tabulated. Clipping used in the proposed method helps improve PAPR of the given information.

Existing Method:

The FFT based MIMO-OFDM systems

Drawbacks:

Low Data Transmission Due To High Bit Loss Interference

Hard decision decoding systems

Proposed Modification:

DWT based MIMO OFDM with turbo decoding various modulation techniques BER analysis

4-QAM, 16QAM, 64 QAM

Fig: MIMO- OFDM with turbo coder

Fig: Turbo Encoder

Fig: Turbo Decoder

Advantages:

  • Iterative soft decision based decoding methodology
  • Time Duration is less
  • Retransmission of data over the network decreased
  • Carrier recovery is high for other application is high

Application:

  • Dual scenario rate capacity networks
  • City coverage area analysis on Improvement through WIFI connection.
  • Medical application through Emergency Analysis on mobile communication

Software Requirement:

  • MATLAB 2014a or above versions

References:-

[1] M. Sandell, C. Luschi, P. Strauch, and R. Yan, .Iterative channel estimation using soft decision feedback,. in IEEE Global Telecommunications Conference, vol. 6, Sydney, NSW, 1998, pp. 3728.3733.

[2] M. Valenti, .Iterative channel estimation for turbo codes over fading channels,. in IEEE Wireless Communications and Networking Conference, vol. 3, 23-28 September 2000, pp. 1019.1024.

[3] B.-L. Yeap, C. Wong, and L. Hanzo, .Reduced complexity inphase/ quadrature-phase M-QAM turbo equalization using iterative channel estimation,. IEEE Transactions on Wireless Communications, vol. 2, no. 1, pp. 2.10, 2003.

[4] S. Song, A. Singer, and K.-M. Sung, .Turbo equalization with an unknown channel,. in Proceedings of IEEE International Conference? on Ascoustics, Speech and Signal Processing, vol. 3, 2002.

[5] .., .Soft input channel estimation for turbo equalization,. IEEE Transactions on Signal Processing, [see also IEEE Transactions on Acoustics, Speech and Signal Processing], vol. 52, pp. 2885.2894, 2004.

[6] R. Otnes and M. T?uchler, .Soft iterative channel estimation for turbo equalization: comparison of channel estimation algorithms,. in The 8th International Conference on Communication Systems, vol. 1, 2002, pp. 72.76.

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