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A robust modulation classification method using convolutional neural networks
2019
EURASIP Journal on Advances in Signal Processing
Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the
doi:10.1186/s13634-019-0616-6
fatcat:tjqs6p2dh5eqxo6hxm32wuqm2q