A CLSTM network algorithm for automatic modulation recognition

Abstract

Aiming at the problems of low recognition accuracy and inexplicable features extracted by neural networks in signal modulation recognition algorithms based on deep learning, this paper proposes a CLSTM recognition algorithm based on IQ signal features. The algorithm first uses a 2×1 convolution kernel to extract the phase feature of the signal, then uses a multiple convolution layer to extract the temporal feature of the signal, and then outputs the result of the convolution layer to the LSTM module to further extract the temporal feature, finally, the recognition result is output by the fully connected layer and the recognition effect is improved by optimizing the network structure. The experimental results show that on the RML2016.10a public dataset, when the SNR is higher than 0 dB, the recognition accuracy of the algorithm reaches 89.21%, which is 15.02% and 9.85% higher than the classical CNN2 and CLDNN network algorithms.