HMT-Net: A Multi-Task Learning Based Framework for Enhanced Convolutional Code Recognition

Due to the critical role of channel coding, convolutional code recognition has attracted growing interest, particularly in non-cooperative communication scenarios such as spectrum surveillance. Deep learning-based approaches have emerged as promising techniques, offering improved classification performance. However, most existing works focus on single-parameter recognition and ignore the inherent correlations between code parameters. To address this, we propose a novel framework named Hybrid Multi-Task Network (HMT-Net), which adopts multi-task learning to simultaneously identify both the code rate and constraint length of convolutional codes. HMT-Net combines dilated convolutions with attention mechanisms and integrates a Transformer backbone to extract robust multi-scale sequence features. It also leverages a Channel-Wise Transformer to capture both local and global information efficiently. Meanwhile, we enhance the dataset by incorporating a comprehensive sequence dataset and further improve the recognition performance by extracting the statistical features of the sequences. Experimental results demonstrate that HMT-Net outperforms single-task models by an average recognition accuracy of 2.89%. Furthermore, HMT-Net exhibits even more remarkable performance, achieving enhancements of 4.57% in code rate recognition and 4.31% in constraint length recognition compared to other notable multi-tasking frameworks such as MAR-Net. These findings underscore the potential of HMT-Net as a robust solution for intelligent signal analysis, offering significant practical value for efficient spectrum management in next-generation communication systems.