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.

翻译：调制识别 (Modulation classification, MC) 是接收端在没有办法从发送端显式地指示调制类型时执行的第一步。近年来，机器学习技术已被广泛用于 MC。在本文中，我们提出了一种新颖的 MC 技术，称为基于星座网络的联合均衡和调制识别 (EMC2-Net)。与以往将星座点视为图像的方法不同，提出的 EMC2-Net 直接使用一组二维星座点来执行 MC。为了在多径衰落信道下获得清晰和具体的星座，所提出的 EMC2-Net 在训练中采用了新颖的三阶段训练和噪声课程预训练，具有独立和可解释的均衡器和分类器的作用。在不同信道模型下进行的线性调制类型的数值结果表明，所提出的 EMC2-Net 在复杂度显著更低的情况下实现了最先进 MC 技术的性能。