| Summary: | Automatic modulation recognition of the multi-system communication signals based on feature extraction and pattern recognition is an important research topic in the field of software radio. It′s one of the key technologies for a complex electromagnetic environment in the field of non-cooperative communications, such as spectrum management, spectrum detection. A new algorithm for communication signals automation modulation recognition based on deep learning is proposed in this paper. It utilizes the autoencoders for feature extraction to obtain feature set with high anti-interference ability, then classifies and identifies the selected features with BP neural network. The algorithm can realize the automatic identification for MQAM communication signal modulation. Simulation results demonstrate that the propsoed algorithm has a good performace in classification and recognition, meanwhile effectively improving the anti-interference ablility of the automatic identification of the digital modulation signal.
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