Automatic Modulation Recognition algorithm based on phase transformation and ResCNN-BiLSTM

Aiming at the problem that the recognition rate of existing automatic modulation recognition models needs to be improved under high signal-to-noise ratio conditions, a model consisting of phase transformation, residual CNN network and bidirectional LSTM network is proposed. First, the DeepSig RadioML 2018.01A dataset is normalized as the model input; the phase parameter is extracted through the phase recognition module, and then the phase is corrected according to the phase parameter; then the spatial features are extracted through the residual CNN network to avoid gradient vanishing and explosion; then the data is passed to the bidirectional LSTM network to extract the bidirectional time series features of the data; finally, the deep neural network is used for classification and recognition. Experimental results show that under high signal-to-noise ratio conditions, the model improves the recognition rate of modulation modes such as 16PSK, and the highest recognition rate and average recognition rate reach 96.79% and 62.13% respectively. Compared with other existing models, the overall optimization of recognition rate and model efficiency is achieved.