  A modulation classification (MC) scheme based on Independent Component
Analysis (ICA) in conjunction with either maximum likelihood (ML) or Support
Vector Machines (SVM) is proposed for MIMO-OFDM signals over frequency
selective, time varying channels. The method is blind in the sense that it is
assumed that the receiver has no information about the channel and transmitted
signals other than that the spatial streams of signals are statistically
independent. The processing consists of separation of the MIMO streams followed
by modulation classification of the separated signals. While in general, blind
separation of signals over frequency selective channels is a difficult problem,
the non-frequency selective nature of the channel experienced by individual
symbols in a MIMO-OFDM system enables the application of well-known ICA
algorithms. Modulation classification is implemented by maximum likelihood and
by an SVM-based modulation classification method relying on pre-selected
modulation-dependent features. To improve performance in time varying channels,
the invariance of the channel is exploited across the coherence bandwidth and
the time coherence. The proposed method is shown to perform with high
probability of correct classification over realistic ITU pedestrian and
vehicular channels. An upper bound on the probability of correct classification
is developed based on the Cramer Rao bound of channel estimation.
