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- W4386952104 abstract "With the rapid development of deep learning (DL), Automatic Modulation Classification (AMC) has also taken a huge leap forward. The DL-based AMC methods are able to achieve high accuracy through training massive labeled samples. However, these DL-based AMC methods would deteriorate dramatically with insufficient samples. Modulation recognition under few sample condition gradually become an urgent problem. To address this problem, we propose a novel learning framework for few-sample AMC, which is termed Multi-Component Extraction Network (MCENet) and can effectively extract potentially and easily distinguishable features. Experimental results on the public available dataset RadioML2016.10a show that the proposed MCENet outperforms other contrastive few-sample AMC methods and achieves better results." @default.
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- W4386952104 date "2023-01-01" @default.
- W4386952104 modified "2023-10-16" @default.
- W4386952104 title "Multi-Component Feature Extraction for Few-Sample Automatic Modulation Classification" @default.
- W4386952104 doi "https://doi.org/10.1109/lcomm.2023.3318288" @default.
- W4386952104 hasPublicationYear "2023" @default.
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