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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… Click to show full abstract
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.
Journal Title: IEEE Communications Letters
Year Published: 2022
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