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Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification

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Abstract

Automatic modulation classification is an essential and challenging topic in the development of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation abilities to sense and learn surrounding environments and make corresponding decisions. In this paper, we propose a spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Since the frequency variation over time is the most important distinction between radio signals with various modulation schemes, we plan to expand samples by introducing different intensities of interference to the spectrum of radio signals. The original signal is first transformed into the frequency domain by using short-time Fourier transform, and the interference to the spectrum can be realized by bidirectional noise masks that satisfy the specific distribution. The augmented signals can be reconstructed through inverse Fourier transform based on the interfered spectrum, and then, the original and augmented signals are fed into the network. Finally, data augmentation at both training and testing stages can be used to improve the generalization performance of deep neural network. To the best of our knowledge, this is the first time that radio signals are augmented to help modulation classification by considering the frequency domain information. Moreover, we have proved that data augmentation at the test stage can be interpreted as model ensemble. By comparing with a variety of data augmentation techniques and state-of-the-art modulation classification methods on the public dataset RadioML 2016.10a, experimental results illustrate the effectiveness and advancement of proposed method.

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Availability of data and materials

All experimental datasets during the study are available online (https://www.deepsig.io/datasets) and have been cited in the paper.

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Funding

This work was supported by National Key R&D Program of China (Grant 2018YFF01014304, 2012YQ20022407), Major Basic Research Project of Shandong Provincial Natural Science Foundation (Grant ZR2019ZD01), and Fundamental Research Founds of Shandong University (Grant 104222019).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, Qingdao, 266237, China

    Qinghe Zheng, Penghui Zhao, Hongjun Wang & Yang Yang

  2. Public (Innovation) Experimental Teaching Center, Shandong University, Qingdao, 266237, China

    Hongjun Wang

  3. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Yang Li

  4. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China

    Yang Li

Authors
  1. Qinghe Zheng
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  2. Penghui Zhao
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  3. Yang Li
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  4. Hongjun Wang
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  5. Yang Yang
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Corresponding authors

Correspondence to Hongjun Wang or Yang Yang.

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The authors declare that they have no conflict of interest.

Code availability

The convergent deep CNN model, test procedure, more visualized convolution kernels, and the public dataset RadioML 2016.10a in MATLAB have been open sourced on GitHub: https://github.com/a1178916307/deep-CNN-for-modulation-classification.git. The full source code and presentation will be released after the paper has been accepted.

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Appendix

Appendix

For the convenience of readers following and understanding this paper, we have annotated all variables in Table 5. In addition, the convergent deep CNN model, test procedure, more visualized convolution kernels, and the public dataset RadioML 2016.10a in MATLAB have been open sourced on GitHub: https://github.com/a1178916307/deep-CNN-for-modulation-classification.git. The full source code and presentation will be released after the paper has been accepted.

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Zheng, Q., Zhao, P., Li, Y. et al. Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Comput & Applic 33, 7723–7745 (2021). https://doi.org/10.1007/s00521-020-05514-1

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