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An Experimental Hyper-Chaos Spread Spectrum Communication System Based on CNN

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

A new hyper-chaos communication system based on cellular neural network (CNN) is proposed in this paper. Hyper-chaos is generated with fifteen-cell CNN, then it’s transferred to a digital sequence. The chaotic sequence is better than gold sequence when they are utilized in direct sequence spread spectrum system. Compared with the traditional gold sequence system, there is 3dB improvement in CNN chaotic sequence system when the channel is multi-path channel. Because of the complex dynamic behavior of hyper-chaos, security signal could be transferred through wireless channel. The structure of hardware CNN spread spectrum system is also shown, and the security of CNN communication is analyzed at last.

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References

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Author information

Authors and Affiliations

  1. Department of Electronics, Peking University, Beijing, 100871, China

    Jianye Zhao, Quansheng Ren & Daoheng Yu

Authors
  1. Jianye Zhao
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  2. Quansheng Ren
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  3. Daoheng Yu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhao, J., Ren, Q., Yu, D. (2006). An Experimental Hyper-Chaos Spread Spectrum Communication System Based on CNN. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_26

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  • DOI: https://doi.org/10.1007/11760191_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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