A novel deep learning automatic modulation classifier with fusion of multichannel information using GRU

Abstract

Automatic modulation classification (AMC) plays a vital role in modern communication systems, which can support wireless communication systems with limited spectrum resource. This paper proposes an AMC method, which integrates gated recurrent unit (GRU) and convolutional neural network (CNN) to utilize the complementary input features of received signals for spatiotemporal feature extraction and classification. Different from other state-of-the-art (SoA) frameworks, the proposed AMC classifier, named as fusion GRU deep learning neural network (FGDNN), aggregates firstly temporal features with GRUs and then extracts spatial features with CNNs. The GRUs can store temporal dynamic features, and facilitate to capture the characteristics of correlation and dependence among input features. The method is tested extensively with comparisons in order to verify its effectiveness. Experiment results show that the recognition rates of our method outperform other deep learning frameworks.