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Modulation classification (MC) is the first step performed at the receiverside unless the modulation type is explicitly indicated by the transmitter.Machine learning techniques have been widely used for MC recently. In thispaper, we propose a novel MC technique dubbed as Joint Equalization andModulation Classification based on Constellation Network (EMC2-Net). Unlikeprior works that considered the constellation points as an image, the proposedEMC2-Net directly uses a set of 2D constellation points to perform MC. In orderto obtain clear and concrete constellation despite multipath fading channels,the proposed EMC2-Net consists of equalizer and classifier having separate andexplainable roles via novel three-phase training and noise-curriculumpretraining. Numerical results with linear modulation types under differentchannel models show that the proposed EMC2-Net achieves the performance ofstate-of-the-art MC techniques with significantly less complexity.

EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Network