Research on recognition of interference signal based on deep learning

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In response to the fact that most traditional communication interference recognition algorithms stay at a shallow learning level and cannot provide a detailed portrayal of the feature information inside the data, this paper proposes a deep convolutional neural network (CNN) based communication interference signal classification and recognition method to achieve the classification and recognition of five types of interference signals. This paper firstly introduces the network structure of CNN, the role of each layer, the convolution principle and common pooling operations, and then, describes the process of CNN-based communication interference signal classification and recognition, and verifies that the CNNbased communication interference signal classification and recognition method has better interference signal recognition rate and robustness through simulation analysis.