ZOU B R, LI S S, YE P R,et al.Modulation classification based on multi-channel residual network and attention mechanism[J].Journal of Henan Polytechnic University(Natural Science) ,2023,42(1):160-167.

doi:10.16186/j.cnki.1673-9787.2021120081

Received:2021/12/22

Revised:2022/01/17

Published:2023/01/25

Modulation classification based on multi-channel residual network and

 attention mechanism

ZOU Borong1, LI Shanshan2, YE Peiran3, HOU Qinghua1, WU Huibin1

1.School of Physics and Electronic Information，Henan Polytechnic University，Jiaozuo  454000，Henan，China；2.School of Information and Electronic Engineering，Shangqiu Institute of Technology，Shangqiu  476000，Henan，China；3.College of Civil Engineering and Architecture，Zhengzhou University of Economics and Business，Zhengzhou  451191，Henan，China

Abstract:In order to more fully extract the characteristics of modulated signals，a multi-channel modulation classification was constructed with the collaboration of residual network and attention mechanism.Firstly，a multi-channel structure with different channels was designed，which ensured that the extracted signal features were more diverse.Secondly，the extracted features of each channel were fused by concatenate layer，which increased the features describing the signal.Then，combining the advantages of residual network，the depth of the network was significantly increased，more representative features were captured，while the problem of gradient disappearance caused by deep network was alleviated.Then，in order to make the above extracted features easier to classify，attention layer was introduced to recalibrate the extracted features to capture more critical features and increase the accuracy of signal classification.An experiment was conducted on the public data set RadioML 2016.10b，and the simulation results showed that the classification performance of the proposed network was better than many classifiers in the literature，with a classification accuracy of 93.23% when the signal-to-noise ratio was equal to 14 dB，this demonstrated its feasibility and effectiveness.

Key words:modulation classification;deep learning;convolutional neural network;residual network;attention mechanism

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