Modulation Classification via Subspace Detection in MIMO Systems

TL;DR

This paper introduces a subspace detection method for modulation classification in MIMO systems, improving accuracy and reducing complexity by decoupling streams and enabling joint detection.

Contribution

It proposes a novel likelihood-based modulation classification approach using subspace decomposition for efficient MIMO signal analysis.

Findings

Outperforms existing modulation classification schemes

Reduces computational complexity

Enables joint modulation classification and data detection

Abstract

The problem of efficient modulation classification (MC) in multiple-input multiple-output (MIMO) systems is considered. Per-layer likelihood-based MC is proposed by employing subspace decomposition to partially decouple the transmitted streams. When detecting the modulation type of the stream of interest, a dense constellation is assumed on all remaining streams. The proposed classifier outperforms existing MC schemes at a lower complexity cost, and can be efficiently implemented in the context of joint MC and subspace data detection.