AMCRN: Few-Shot Learning for Automatic Modulation Classification

doi:10.1109/LCOMM.2021.3135688

AMCRN: Few-Shot Learning for Automatic Modulation Classification

2022-03-01

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DOI：10.1109/LCOMM.2021.3135688

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Abstract

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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.

Keywords:

Modulation

Training

Feature extraction

Signal to noise ratio

Convolutional neural networks

Convolution

Kernel

Few shot learning

automatic modulation classification

relation network

deep learning

AMCRN: Few-Shot Learning for Automatic Modulation Classification

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