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AMCRN: Few-Shot Learning for Automatic Modulation Classification
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DOI：10.1109/LCOMM.2021.3135688.png)
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摘要
En 中文
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.
Keyword:
Modulation
Training
Feature extraction
Signal to noise ratio
Convolutional neural networks
Convolution
Kernel
Few shot learning
automatic modulation classification
relation network
deep learning
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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