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Automatic Digital Modulation Recognition Using Support Vector Machines and Genetic Algorithm

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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 new method, based on support vector machines (SVMs) and genetic algorithm (GA), is proposed for automatic digital modulation recognition (ADMR). In particular, the best feature subset from the combined statistical feature set and spectral feature set is optimized using genetic algorithm. Compared to the conventional artificial neural network (ANN) method, the method proposed avoids overfitting and local optimal problems. Simulation results show that this method is more robust and effective than other existing approaches, particularly at a low signal noise ratio (SNR).

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References

  1. Nandi, A.K., Azzouz, E.E.: Modulation Recognition Using Artificial Networks. IEEE Trans. Signal processing 56, 165–175 (1997)

    MATH  Google Scholar 

  2. Nandi, A.K., Azzouz, E.E.: Algorithms for Automatic Modulation Recognition of Communication Signals. IEEE Trans. Communications 46, 431–435 (1998)

    Article  Google Scholar 

  3. Swami, A., Sadler, B.M.: Hierarchical Digital Modulation Classification Using Cumulants. IEEE Trans. Communications 48, 416–429 (2000)

    Article  Google Scholar 

  4. Vapnik, V.: An Overview of Statistical Learning Theory. IEEE Trans. Neural Networks 10, 988–999 (1999)

    Article  Google Scholar 

  5. Klautau, A., Jevtic, N., Orlitsky, A.: On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines. Journal of Machine Learning Research 4, 1–15 (2003)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Cheng du, Sichuan, 610054, China

    Jie Li, Heng Chu & Weile Zhu

  2. School of Network Technology, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050061, China

    Jing Peng

Authors
  1. Jie Li
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  2. Jing Peng
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  3. Heng Chu
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  4. Weile Zhu
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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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Li, J., Peng, J., Chu, H., Zhu, W. (2005). Automatic Digital Modulation Recognition Using Support Vector Machines and Genetic Algorithm. 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_93

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  • DOI: https://doi.org/10.1007/11427445_93

  • 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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