Abstract: Aiming at the problem that most of the existing modulation classification algorithms ignore the complementarity between different features and feature fusion,this paper proposes a method of feature fusion using deep learning model.This method attempts to fuse the temporal and spatial features of modulated signals to obtain more distinct recognition features.First,the A/P signal and I/Q signal of the modulation signal are obtained.Then,the convolution long-term and short-term memory module and the complex dense residual convolution module are built to extract the temporal features of A/P signal and the spatial features of I/Q signal respectively,and fuse them to obtain the fusion complementary recognition features.Finally,the recognition features are input into the classification network to obtain the recognition results.Experimental results show that based on the open source data set,when the signal-to-noise ratio is greater than 5 dB,the recognition rate reaches 93.25%,and the recognition accuracy is 3%~11% higher than that based on single feature recognition.The actual collected data is used for classification and recognition,which further proves the effectiveness of the proposed feature extraction model and fusion strategy.

Key words: Automatic modulation classification, Multimodal fusion, Deep learning