Automatic Channel Coding Recognition Based on DeepVGG 


Vol. 50,  No. 3, pp. 420-427, Mar.  2025
10.7840/kics.2025.50.3.420


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  Abstract

This paper proposes a deep learning-based automatic channel coding recognition method. Channel coding is a crucial technology in wireless communication systems that enhances communication quality through error correction, ensuring reliable data transmission. To overcome the limitations of traditional one-dimensional data processing methods, this study utilizes a DeepVGG model to convert one-dimensional channel coding data into a two-dimensional format, thereby improving recognition performance. The proposed method maintains high recognition accuracy even in low SNR environments and shows an average performance improvement of over 10% compared to TextCNN and BiLSTM-CNN models. Notably, it demonstrates superior classification performance for seven types of channel coding and has the potential to contribute to real-time channel coding recognition in communication systems.

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[IEEE Style]

Y. Cheon and W. Lim, "Automatic Channel Coding Recognition Based on DeepVGG," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 3, pp. 420-427, 2025. DOI: 10.7840/kics.2025.50.3.420.

[ACM Style]

Yurim Cheon and Wansu Lim. 2025. Automatic Channel Coding Recognition Based on DeepVGG. The Journal of Korean Institute of Communications and Information Sciences, 50, 3, (2025), 420-427. DOI: 10.7840/kics.2025.50.3.420.

[KICS Style]

Yurim Cheon and Wansu Lim, "Automatic Channel Coding Recognition Based on DeepVGG," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 3, pp. 420-427, 3. 2025. (https://doi.org/10.7840/kics.2025.50.3.420)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880