In order to solve the problem that the current recognition algorithm can only recognize one or two code types and the complexity of manually extracting features,two channel coding type recognizers based on the deep neural network model are proposed,namely,convolutional neural network(CNN)recognizer and recursive CNN(RCNN)recognizer,used to identify different types of channel codewords in received data.The soft demodulation sequence to be recognized is treated as the sentence vector of text classification in natural language processing,input into the pre trained deep neural network recognizer for recognition,and analyze the influence of word length on recognition accuracy,and obtain the most appropriate word length.The experimental results show that both types of recognizers can effectively recognize various types of channel codes in the received data,and the recognition accuracy of the CNN recognizer can reach over 99％when the signal-to-noise ratio is 3 dB,while the RCNN recognizer can achieve over 99％recognition accuracy at 1 dB.
关键词
深层神经网络/信道编码识别器/盲识别/字长度
Key words
deep neural networks/channel code recognizer/blind identification/word length
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