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

TL;DR

EMC2-Net introduces a novel approach for modulation classification that directly utilizes constellation points, combining equalization and classification in a unified network to improve performance and reduce complexity.

Contribution

The paper presents EMC2-Net, a new method that jointly performs equalization and modulation classification using constellation points, with a novel three-phase training process.

Findings

Achieves state-of-the-art modulation classification performance.

Reduces computational complexity compared to existing methods.

Effectively handles multipath fading channels with improved constellation clarity.

Abstract

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.