Best Papers
 CRANet-Based Blind Recognition of Channel Coding 


Vol. 50,  No. 4, pp. 578-586, Apr.  2025
10.7840/kics.2025.50.4.578


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  Abstract

Blind channel coding recognition is highly useful in non-cooperative communication environments, such as cyber-electronic warfare. Blind channel coding recognition is a technique where the receiver identifies the channel coding scheme without prior knowledge of the coding or any additional data processing. In this paper, we propose CRANet, a network designed to improve channel coding recognition rates in blind environments by utilizing CNN, Residual, and Attention mechanisms. Eight types of channel coding schemes were used: BCH, Hamming, Product, RM, Polar, Golay, Convolutional, and Turbo. Simulation results show that the proposed CRANet outperforms benchmark deep learning models such as TextCNN and CNN-BLSTM, with accuracy improvements of up to 53.5% and 58.7%, respectively. Moreover, when using 2D CNN instead of 1D CNN, the recognition performance improved by 41.36% at –4 dB. Notably, CRANet with 2D CNN achieved an accuracy of 93.62% at 0dB.

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[IEEE Style]

S. Shin and W. Lim, "CRANet-Based Blind Recognition of Channel Coding," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 578-586, 2025. DOI: 10.7840/kics.2025.50.4.578.

[ACM Style]

Saebin Shin and Wansu Lim. 2025. CRANet-Based Blind Recognition of Channel Coding. The Journal of Korean Institute of Communications and Information Sciences, 50, 4, (2025), 578-586. DOI: 10.7840/kics.2025.50.4.578.

[KICS Style]

Saebin Shin and Wansu Lim, "CRANet-Based Blind Recognition of Channel Coding," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 4, pp. 578-586, 4. 2025. (https://doi.org/10.7840/kics.2025.50.4.578)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880