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<p>Facing heterogeneous signals increasing in dynamic spectrum, cognitive radio urgently needs blind channel coding identification. This technology addresses the core challenge of unknown coding schemes in non-cooperative communications. Existing methods are typically restricted to specific coding types and suffer from poor identification accuracy and robustness. To mitigate this constraint, we propose a Dual-Branch Feature Fusion Convolutional Neural Network (DBFCNN) framework for fine-grained identification among seven common channel-coding schemes. The network adopts a two-branch architecture. One branch employs multi-scale dilated convolutions to extract long-range dependencies in the received bit sequence, the other is a statistical branch that extract descriptors such as run length, entropy values, coding depth and so on to expose code-specific algebraic characteristics. The fused representation is fed to a fully connected classifier to jointly identify the seven code types. Extensive simulations demonstrate that DBFCNN improves identification accuracy by about 5% (absolute) over a strong prior baseline under comparable settings, proving the feasibility and effectiveness of the method.</p>

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Blind recognition of channel codes based on dual-branch feature fusion convolutional neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Yuwei Ma,
  • Yingke Lei,
  • Changming Liu,
  • Wei Wang,
  • Fei Teng,
  • Chuang Peng,
  • Hu Jin,
  • Hui Feng,
  • Mengbo Zhang,
  • Yu Pan

摘要

Facing heterogeneous signals increasing in dynamic spectrum, cognitive radio urgently needs blind channel coding identification. This technology addresses the core challenge of unknown coding schemes in non-cooperative communications. Existing methods are typically restricted to specific coding types and suffer from poor identification accuracy and robustness. To mitigate this constraint, we propose a Dual-Branch Feature Fusion Convolutional Neural Network (DBFCNN) framework for fine-grained identification among seven common channel-coding schemes. The network adopts a two-branch architecture. One branch employs multi-scale dilated convolutions to extract long-range dependencies in the received bit sequence, the other is a statistical branch that extract descriptors such as run length, entropy values, coding depth and so on to expose code-specific algebraic characteristics. The fused representation is fed to a fully connected classifier to jointly identify the seven code types. Extensive simulations demonstrate that DBFCNN improves identification accuracy by about 5% (absolute) over a strong prior baseline under comparable settings, proving the feasibility and effectiveness of the method.