Efficient near optimal joint modulation classification and detection for MU-MIMO systems

Optimum data detection schemes for dual layer multi-user multiple-input multiple-output (MU-MIMO) systems are studied. A joint maximum likelihood (ML) modulation classification (MC) of the co-scheduled user and data detection receiver is developed. By expanding the max-log-maximum-a-posteriori MC approach to include distances of counter ML hypothesis symbols, the decision metric for MC is shown to be an accumulation over a set of tones of Euclidean distance computations also used by the ML detector for bit log-likelihood ratio soft decision generation. With a small complexity overhead, the proposed approach achieves near-optimal performance. An efficient hardware architecture is presented for the proposed approach.