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Automatic Digital Modulation Recognition Based on ART2A-DWNN

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

A novel automatic digital modulation recognition classifier combining adaptive resonance theory 2A (ART2A) with discrete wavelet neural network (DWNN), called ART2A-DWNN, is proposed in this paper. The modified ART2A network with a low vigilance parameter is used to categorize input modulation schemes into some classes and then DWNN is employed in each class to recognize modulation schemes. Moreover, error back propagation (BP) learning algorithm with momentum is adopted in DWNN to speed up the training phase and improve the convergence capability. Simulation results obtained from modulated signals corrupted with Gaussian noise at 8dB Signal to Noise Ratio (SNR) are given to evaluate the performance of the proposed method and it is found that the benefits of the developed method include improvement of recognition capability, training convergence enhancement and easiness to accommodate new patterns without forgetting old ones.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China

    Zhilu Wu, Xuexia Wang, Cuiyan Liu & Guanghui Ren

Authors
  1. Zhilu Wu
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  2. Xuexia Wang
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  3. Cuiyan Liu
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  4. Guanghui Ren
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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. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

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

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Wu, Z., Wang, X., Liu, C., Ren, G. (2005). Automatic Digital Modulation Recognition Based on ART2A-DWNN. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_62

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  • DOI: https://doi.org/10.1007/11427445_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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