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Deep Learning based Channel Code Recognition using TextCNN

The recognition of channel code of primary user signal is a important task for the full awareness of wireless environment in cognitive radio. Previous solutions to this problem usually suffer from high computational complexity that is not suitable for real-time applications and manual feature extraction that requires experience and expertise. This paper proposes a deep learning based channel code recognition algorithm that extracts features automatically and avoids complicated calculation. Three convolutional codes are considered as the candidate codes. To recognize which channel code has been adopted by the primary user, the received sequence is regarded as a text sentence and then understood by TextCNN. Experimental results show that the proposed algorithm works well and outperforms the max-log-MAP decoding algorithm in recognition accuracy.