Automatic modulation classification for MIMO system based on the mutual information feature extraction

TL;DR

This paper introduces a novel automatic modulation classification method for MIMO systems that leverages mutual information features from IQ diagrams, reducing the need for extensive training and interference mitigation.

Contribution

The paper proposes a new AMC algorithm based on mutual information feature extraction from IQ diagrams, addressing interference issues without requiring large training datasets.

Findings

Effective in classifying MIMO signals with limited training data

Utilizes mutual information to capture variable interdependencies

Proven applicability on real-world datasets

Abstract

Automatic Modulation Classification (AMC) is an essential technology that is widely applied into various communications scenarios. In recent years, many Machine Learning and Deep-Learning methods have been introduced into AMC, and a lot of them apply different approaches to eliminate interference in complex Multiple-Input and Multiple-Output (MIMO) signals and improve classification performance. However, in practical communication systems, the perfect elimination of MIMO signal interference is impossible, and therefore classification performance suffers. In this paper, we propose a new AMC algorithm for MIMO system based on mutual information (MI) features extraction, which does not require a large amount of training data and the elimination of MIMO signal interference. In this approach, features based on mutual information are extracted using In-Phase and Quadrature (IQ) constellation diagrams of MIMO signals, which have not been explored previously. Our method can be effective since mutual information considers the interdependencies among variables and measures how much information about one variable reduces uncertainty about another, providing a valuable perspective for extracting higher-level and interesting features from the data. The effectiveness of our method is evaluated on several model and real-world datasets, and its applicability is proven.