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：调制分类（MC）是接收端执行的首要步骤，除非发射端明确指示调制类型。近年来，机器学习技术已被广泛应用于调制分类。本文提出一种名为"基于星座网络的联合均衡与调制分类"（EMC2-Net）的新型调制分类技术。与先前将星座点视为图像的研究不同，本文提出的EMC2-Net直接利用二维星座点集合进行调制分类。为在多径衰落信道下获得清晰具体的星座图，所提EMC2-Net通过新颖的三阶段训练与噪声课程预训练方法，使均衡器和分类器具备独立且可解释的功能。在不同信道模型下的线性调制类型数值结果表明，所提EMC2-Net在显著降低复杂度的同时，达到了与现有最优调制分类技术相当的性能。