Automatic Modulation Recognition Algorithm Based on Feature Integration
Most of the present neural network-based modulation recognition algorithms use the time domain or frequency domain as the single informationsource and ignore the use of multiple transform domaininformation features for complementary advantages.This article proposes a deep learning automatic modulation recognition algorithm based on feature integration,which can effectively improve the modulation recognition effect of by using time domain or fre-quency domain as the single information source.The algorithm includes the time-frequency feature extraction module,which integrates the features of signals in different transform domains.Then,the long short-term memoryneu-ral network based on the attention mechanism and the full connection layer were used for classification.Complementa-ry advantages were achieved through the feature integration of multiple transform domain informations.The simulation experiments show that the automatic modulation recognition algorithm based on feature integration can effectively ex-tract the signal features with improved recognition accuracy,compared with that of traditional deep learning modulation recognition algorithm.
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