Automatic Modulation Classification Method Based on Deep Learning Network Fusion

‍ ‍Although the automatic modulation classification （AMC） method based on deep learning network can achieve satisfactory classification results for most communication modulation signals， it is not ideal for the classification of WBFM signals and MQAM signals. Aiming at the problem of misjudgment of WBFM signal， the decision method is used to screen it. Considering the imbalance of signal samples，a data enhancement method is introduced to expand the screened WBFM signals. To solve the problem of confusion of MQAM signals， fractional Fourier transform （FRFT） is adopted to obtain more feature information of time-frequency dimension. On this basis，a multi-channel feature fusion network that is composed of feature pyramid network and long short-term memory network is proposed to extract deep and shallow features for classification. The experiment results show that the proposed method can solve the problem of misjudgment of WBFM signals and the confusion of MQAM signals to a certain extend. Compared with CNN， ResNet， LSTM and CLDNN networks， the proposed network has higher classification accuracy.