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