A modulation classification scheme based on Independent Component Analysis (ICA) in conjunction with 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 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.
['Handan Agirman-Tosun', 'Yu Liu', 'Alexander M. Haimovich', 'Osvaldo Simeone', 'Wei Su', 'Jason A. Dabin', 'Emmanuel Kanterakis']
Modulation classification of MIMO-OFDM signals by Independent Component Analysis and Support Vector Machines