EMC2-Net: Joint Equalization and Modulation Classification based on Constellation Network
This addresses the problem of efficient and accurate modulation classification for wireless communication receivers, representing an incremental improvement over prior methods.
The paper tackles modulation classification in multipath fading channels by proposing EMC2-Net, a method that jointly performs equalization and classification using constellation points directly, achieving state-of-the-art performance with significantly reduced complexity.
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.