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Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals

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Cognitive Radio Oriented Wireless Networks (CROWNCOM 2018)

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Abstract

Cognitive Radio (CR) systems allow optimizing the use of the shared radio spectrum and enhancing the coexistence among different technologies by efficiently changing certain operating parameters of the radios such as transmit-power, carrier frequency, and modulation and coding scheme in real-time. Dynamic Spectrum Access (DSA), which allows radios to dynamically access and use the unused spectrum, is one of the tasks that are fundamental for a better use of the spectrum. In this paper, we extend the previous work on Automatic Modulation Classification (AMC) by using Deep Neural Network (DNNs) and evaluate the performance of these architectures on signals that are not only modulated but are also encoded. We call this the Automatic Modulation and Coding Scheme Classification problem, or \(AMC^2\). In this problem, radio signals are classified according to the Modulation and Coding Scheme (MCS) used during their transmission. Evaluations on a data set of 802.11 radio signals, transmitted with different MCS and Signal to Noise Ratio (SNR), provide important results on the impact of some DNN hyperparameters, e.g. number of layers, batch size, etc., in the classification accuracy.

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Authors and Affiliations

  1. University of Antwerp, Antwerp, Belgium

    Phui San Cheong

  2. IDLab Research Group, Department of Mathematics and Computer Science, University of Antwerp - imec, Antwerp, Belgium

    Miguel Camelo & Steven Latré

Authors
  1. Phui San Cheong
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  2. Miguel Camelo
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  3. Steven Latré
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Corresponding author

Correspondence to Miguel Camelo .

Editor information

Editors and Affiliations

  1. Ghent University, Ghent, Belgium

    Ingrid Moerman

  2. University of Antwerp, Antwerpen, Belgium

    Johann Marquez-Barja

  3. Ghent University, Zwijnaarde, Belgium

    Adnan Shahid

  4. Ghent University, Zwijnaarde, Belgium

    Wei Liu

  5. Ghent University, Zwijnaarde, Belgium

    Spilios Giannoulis

  6. Ghent University, Zwijnaarde, Belgium

    Xianjun Jiao

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cheong, P.S., Camelo, M., Latré, S. (2019). Evaluating Deep Neural Networks to Classify Modulated and Coded Radio Signals. In: Moerman, I., Marquez-Barja, J., Shahid, A., Liu, W., Giannoulis, S., Jiao, X. (eds) Cognitive Radio Oriented Wireless Networks. CROWNCOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 261. Springer, Cham. https://doi.org/10.1007/978-3-030-05490-8_17

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  • DOI: https://doi.org/10.1007/978-3-030-05490-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05489-2

  • Online ISBN: 978-3-030-05490-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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