TY  - JOUR
T1  - Automatic Channel Coding Recognition Based on DeepVGG
AU  - Cheon, Yurim 
AU  - Lim, Wansu 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.3.420
KW  - Deep Learning
KW  - Channel Coding
KW  - 2D-CNN
KW  - DeepVGG
KW  - Wireless Communication Systems
KW  - Blind Recognition
AB  - 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.