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Chaos Prediction of Fast Fading Channel of Multi-rates Digital Modulation Using Support Vector Machines

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Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10603))

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Abstract

According to the support vector domain properties, the paper establishes vector domain predictive models of chaos channel as well as chaos phase trace of non-linear map, the chaotic fading channel model was established based on Takens phase space delay reconstructing theory. Self-learning makes error least upper bound of generalization model to be minimum. The non-linear higher dimension map was realized by the squares support vector domain. The future fading channel data was predicted from training data set. The predictive error changes with the increase of embed dimension to a constant. The experiment result indicates that the support vector domain needs little support vector with fast convergence rate. With the small sample and unknown probability density, the multi-path predictive series consisted with true value series in Doppler fast fading channel. Under the conditions of small sample, the predicted series is in concordance with the channel true value.

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Acknowledgements

Supported by the National Natural Science Foundation of China under Grant No 61574115. Shaanxi Natural Science Basic Research Plan in Shaanxi Province of China (2016JM1029), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, PAPD, CICAEET.

Author information

Authors and Affiliations

  1. Internet of Thing, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yijing Ren

  2. Department of Optics Information, Xi’an Jiaotong University, Xi’an, 710054, China

    Yijing Ren & Ren Ren

Authors
  1. Yijing Ren
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  2. Ren Ren
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Correspondence to Ren Ren .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Ren, Y., Ren, R. (2017). Chaos Prediction of Fast Fading Channel of Multi-rates Digital Modulation Using Support Vector Machines. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_68

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  • DOI: https://doi.org/10.1007/978-3-319-68542-7_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68541-0

  • Online ISBN: 978-3-319-68542-7

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