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

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.