A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

Applied Sciences

Siyang Zhou¹  Rui Zhang¹  Zhendong Yin¹  Zhilu Wu¹ 

[1] Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China;

关键词: Automatic Modulation Classification;    attention mechanism;    Convolution Neural Network;    gate recurrent unit;    AM-Softmax;    deep learning;   

DOI  :  10.3390/app11031327

来源: DOAJ

【 摘 要 】

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

【 授权许可】

Unknown