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A modulation classification (MC) scheme based on Independent ComponentAnalysis (ICA) in conjunction with either maximum likelihood (ML) or SupportVector Machines (SVM) is proposed for MIMO-OFDM signals over frequencyselective, time varying channels. The method is blind in the sense that it isassumed that the receiver has no information about the channel and transmittedsignals other than that the spatial streams of signals are statisticallyindependent. The processing consists of separation of the MIMO streams followedby modulation classification of the separated signals. While in general, blindseparation of signals over frequency selective channels is a difficult problem,the non-frequency selective nature of the channel experienced by individualsymbols in a MIMO-OFDM system enables the application of well-known ICAalgorithms. Modulation classification is implemented by maximum likelihood andby an SVM-based modulation classification method relying on pre-selectedmodulation-dependent features. To improve performance in time varying channels,the invariance of the channel is exploited across the coherence bandwidth andthe time coherence. The proposed method is shown to perform with highprobability of correct classification over realistic ITU pedestrian andvehicular channels. An upper bound on the probability of correct classificationis developed based on the Cramer Rao bound of channel estimation.

Modulation Classification of MIMO-OFDM Signals by Independent Component Analysis and Support Vector Machines