Deep Learning-Based Modulation Signal Detection Scheme Via Transfer Learning for Cognitive Radio Network 


Vol. 45,  No. 10, pp. 1708-1711, Oct.  2020
10.7840/kics.2020.45.10.1708


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  Abstract

A significant amount of labeled training data is required to implement a superior deep learning-based signal detector for cognitive radio network. In general, however, training data is often not sufficiently guaranteed depending on modulation. In this paper, we propose the deep learning-based modulation signal detection scheme via transfer learning to effectively detect a signal of the primary user with less training data in a cognitive radio network. The proposed scheme also takes into account a convolutional neural network model using the similarity between modulation signals through dynamic time warping.

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  Cite this article

[IEEE Style]

J. Park, Y. Choi, D. Seo, J. Ahn, H. Nam, "Deep Learning-Based Modulation Signal Detection Scheme Via Transfer Learning for Cognitive Radio Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 10, pp. 1708-1711, 2020. DOI: 10.7840/kics.2020.45.10.1708.

[ACM Style]

Jiyeon Park, Yukyung Choi, Dongho Seo, Junil Ahn, and Haewoon Nam. 2020. Deep Learning-Based Modulation Signal Detection Scheme Via Transfer Learning for Cognitive Radio Network. The Journal of Korean Institute of Communications and Information Sciences, 45, 10, (2020), 1708-1711. DOI: 10.7840/kics.2020.45.10.1708.

[KICS Style]

Jiyeon Park, Yukyung Choi, Dongho Seo, Junil Ahn, Haewoon Nam, "Deep Learning-Based Modulation Signal Detection Scheme Via Transfer Learning for Cognitive Radio Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 10, pp. 1708-1711, 10. 2020. (https://doi.org/10.7840/kics.2020.45.10.1708)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880