Multiple-Input Multiple-Output (MIMO) digital communications standards usually acquire Channel State Information (CSI) by means of supervised algorithms, which implies loss of performance since pilot symbols do not convey information. We propose obtaining this CSI by using semi-blind techniques, which combine both supervised and unsupervised (blind) methods. The key idea consists in introducing a decision criterion to determine when the channel suffered a significant change. In such a case, transmission of pilot symbols is required. The use of this criterion also allows us to determine the time instants in which CSI has to be sent to the transmitter from the receiver through a low-cost feedback channel.
This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG).