AMCRN: Few-Shot Learning for Automatic Modulation Classification
Quan Zhou, Ronghui Zhang, Junsheng Mu, Hongming Zhang, Fangpei Zhang, Xiaojun Jing
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.
Topics & Concepts
Computer scienceModulation (music)Artificial intelligenceSignal-to-noise ratio (imaging)Noise (video)Pattern recognition (psychology)One shotShot (pellet)TelecommunicationsImage (mathematics)PhysicsAcousticsMechanical engineeringEngineeringOrganic chemistryChemistryWireless Signal Modulation ClassificationAdvanced biosensing and bioanalysis techniques