  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.
