@article{M327A9508,
title = "Automatic Channel Coding Recognition Based on DeepVGG",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.3.420",
author = "Yurim Cheon, Wansu Lim",
keywords = "Deep Learning, Channel Coding, 2D-CNN, DeepVGG, Wireless Communication Systems, Blind Recognition",
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."
}