Open AccessDenseNet-ResNet-LSTM model for modulation recognition of communication signal
Author(s) -
ZongYu Li,
YanDong Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1693/1/012150
Subject(s) - computer science , generalization , artificial intelligence , pattern recognition (psychology) , residual neural network , modulation (music) , artificial neural network , feature (linguistics) , feature extraction , signal (programming language) , noise (video) , speech recognition , image (mathematics) , mathematics , mathematical analysis , philosophy , linguistics , programming language , aesthetics
The traditional artificial neural network provides a low recognition rate in the modulation recognition of communication signals, suffering from the difficulty of feature extraction. Also, it requires a high signal-to-noise ratio (SNR). In order to solve these problems, this paper proposes the combined model: DenseNet-ResNet-LSTM. In the proposed model, DenseNet and ResNet extract different spatial features of samples, and then LSTM extracts the sequence of samples. Also, the attention mechanism is employed to improve the learning efficiency and ability to learn important features. Experimental results show that the proposed model achieves higher accuracy and better generalization ability over the CNN-LSTM network.