With the significant increase in the complexity and diversity of modulation types in modern wireless communication environments,higher requirements are placed on the performance of automatic modulation recognition technology.This paper proposes a hybrid neural network model consisting of a convolutional neural network,a squeeze and excitation module,a long short-term memory network,a gated recurrent unit,and a fully connected layer network to improve the efficiency and accuracy of AMR technology.First,to address the problem of limited modulation signal recognition accuracy in low signal-to-noise ratio environments,a singular value decomposition algorithm is introduced to denoise the received I/Q signal,thereby improving the recognition accuracy of modulation signals under low signal-to-noise ratios while improving signal quality.Then,a convolutional neural network is used to extract multi-channel spatial features from the denoised signal.Then,a squeeze and excitation module is added to improve the pertinence of feature extraction.The gated recurrent unit and the long short-term memory network are combined to capture the time series characteristics of the signal.Finally,the extracted features are mapped to the classification space of the modulation mode through a fully connected layer network for classification and recognition.Experimental results show that the proposed network model significantly improves the modulation recognition accuracy in a low signal-to-noise ratio environment.The average recognition accuracy on the RadioML2016.10b dataset reaches 64.63%.At the same time,it enhances and improves the distinction and recognition accuracy of QAM16 and QAM64.
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