A Novel Method for Complex-Valued Signals in Independent Component Analysis Framework

This paper deals with the separation problem of complex-valued signals in the independent component analysis (ICA) framework, where sources are linearly and instantaneously mixed. Inspired by the recently proposed reference-based contrast criteria based on kurtosis, a new contrast function is put forward by introducing the reference-based scheme to negentropy, based on which a novel fast fixed-point (FastICA) algorithm is proposed. This method is similar in spirit to the classical negentropy-based FastICA algorithm, but differs in the fact that it is much more efficient than the latter in terms of computational speed, which is significantly striking with large number of samples. Furthermore, compared with the kurtosis-based FastICA algorithms, this method is more robust against unexpected outliers, which is particularly obvious when the sample size is small. The local consistent property of this new negentropic contrast function is analyzed in detail, together with the derivation of this novel algorithm presented. Performance analysis and comparison are investigated through computer simulations and realistic experiments, for which a simple wireless communication system with two transmitting and receiving antennas is constructed.