An Efficient Likelihood-Based Modulation Classification Algorithm for MIMO Systems

TL;DR

This paper introduces a new likelihood-based modulation classification algorithm for MIMO systems that improves recognition accuracy and computational efficiency by employing MMSE filtering and a global likelihood maximization approach.

Contribution

The paper develops a novel likelihood-based MC algorithm for MIMO signals that overcomes limitations of existing methods and achieves near-optimal performance with manageable complexity.

Findings

Performs close to the performance upper bound

Works well under various operating conditions

Has reasonable computational complexity

Abstract

Blind algorithms for multiple-input multiple-output (MIMO) signals interception have recently received considerable attention because of their important applications in modern civil and military communication fields. One key step in the interception process is to blindly recognize the modulation type of the MIMO signals. This can be performed by employing a Modulation Classification (MC) algorithm, which can be feature-based or likelihood-based. To overcome the problems associated with the existing likelihood-based MC algorithms, a new algorithm is developed in this paper. We formulated the MC problem as maximizing a global likelihood function formed by combining the likelihood functions for the estimated transmitted signals, where Minimum Mean Square Error (MMSE) filtering is employed to separate the MIMO channel into several sub-channels. Simulation results showed that the proposed algorithm works well under various operating conditions, and performs close to the performance upper bound with reasonable complexity.