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Automatic Classification of Digitally Modulated Signals Based on K-Nearest Neighbor

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Future Information Technology - II

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 329))

Abstract

In this paper, we propose an automatic classification method for eight digitally modulated signals, such as 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK, 16QAM, and 64QAM. The method uses spectral correlation density and high-order cumulants as features. For feature classification, K-nearest neighbor algorithm is used. Simulation results are demonstrated to evaluate the proposed scheme.

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Acknowledgments

This work was supported by the Agency for Defense Development of Korea.

Author information

Authors and Affiliations

  1. Department of Electronics Engineering, Chungbuk National University, Cheongju, Korea

    Woo-Hyun Ahn & Bo-Seok Seo

  2. Agency for Defense Development of Korea, Daejeon, Korea

    Sun-Phil Nah

Authors
  1. Woo-Hyun Ahn
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  2. Sun-Phil Nah
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  3. Bo-Seok Seo
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Corresponding author

Correspondence to Bo-Seok Seo .

Editor information

Editors and Affiliations

  1. Seoul National University of Science and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. Department of Computer Science, Georgia State University, Atlanta, Georgia, USA

    Yi Pan

  3. Digital Media Engineering, Anyang University, Anyang, Korea, Republic of (South Korea)

    Cheonshik Kim

  4. Information & Communication Technologies, Swinburne University of Technology, Melbourne, Victoria, Australia

    Yun Yang

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© 2015 Springer Science+Business Media Dordrecht

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Ahn, WH., Nah, SP., Seo, BS. (2015). Automatic Classification of Digitally Modulated Signals Based on K-Nearest Neighbor. In: Park, J., Pan, Y., Kim, C., Yang, Y. (eds) Future Information Technology - II. Lecture Notes in Electrical Engineering, vol 329. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9558-6_8

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  • DOI: https://doi.org/10.1007/978-94-017-9558-6_8

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-9557-9

  • Online ISBN: 978-94-017-9558-6

  • eBook Packages: EngineeringEngineering (R0)

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