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Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN

by 1,2, 1,2, 1,2 and 1,2,*
1
State Key Lab for Electronic Testing Technology, North University of China, Taiyuan 030051, China
2
School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Academic Editor: Ernestina Menasalvas
Received: 11 April 2022 / Revised: 8 May 2022 / Accepted: 12 May 2022 / Published: 15 May 2022
(This article belongs to the Section Signal and Data Analysis)
AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field. View Full-Text
Keywords: automatic modulation classification; feature fusion; gram angular field; deep learning; convolutional neural network automatic modulation classification; feature fusion; gram angular field; deep learning; convolutional neural network
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MDPI and ACS Style

Bai, J.; Yao, J.; Qi, J.; Wang, L. Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN. Entropy 2022, 24, 700. https://0-doi-org.brum.beds.ac.uk/10.3390/e24050700

AMA Style

Bai J, Yao J, Qi J, Wang L. Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN. Entropy. 2022; 24(5):700. https://0-doi-org.brum.beds.ac.uk/10.3390/e24050700

Chicago/Turabian Style

Bai, Jiansheng, Jinjie Yao, Juncheng Qi, and Liming Wang. 2022. "Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN" Entropy 24, no. 5: 700. https://0-doi-org.brum.beds.ac.uk/10.3390/e24050700

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