Likelihood-Based Modulation Classification for MU-MIMO Systems

The problem of optimal likelihood based modulation classification (MC) for optimal detection in 2x2 multiuser MIMO (MU-MIMO) receivers is considered. The optimal Log-MAP classifier is computationally exhaustive, and its sub-optimal Max-Log-MAP version poses remarkable degradation in performance. Between these two extremes, we propose four computationally efficient methods for MC, by taking special subset constellation points for Euclidean distance computations that constitute the decision metric. Compared to the Max-Log-MAP classifier, the proposed schemes achieved a frame error rate (FER) gain of 0.5dB with uncorrelated channels, while the gains reach 2dB under highly correlated channel conditions.