AMCRN: Few-Shot Learning for Automatic Modulation Classification

Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely aut...

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Bibliographic Details
Published in:IEEE communications letters 2022-03, Vol.26 (3), p.542-546
Main Authors: Zhou, Quan, Zhang, Ronghui, Mu, Junsheng, Zhang, Hongming, Zhang, Fangpei, Jing, Xiaojun
Format: Article
Language:English
Subjects:
automatic modulation classification
Classification
Computer architecture
Convolution
Convolutional neural networks
Deep learning
Feature extraction
Few shot learning
Kernel
Machine learning
Modulation
relation network
Signal to noise ratio
Training
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Summary:Deep learning (DL) has been widely applied in automatic modulation classification (AMC), while the superb performance highly depends on high-quality datasets. Motivated by this, the AMC under few-shot conditions is considered in this letter, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods. Experimental results state that the accuracy of proposed AMCRN exceeds 90% and 10% to 50% improvements are obtained compared with classical schemes when the signal-to-noise ratio (SNR) is greater than −2 dB.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3135688