Semiblind channel estimation for MIMO spatial multiplexing systems

For the case of white uncorrelated inputs, most of the blind multichannel identification techniques are not very robust and only allow to estimate the channel up to a number of ambiguities, especially in the MIMO case. On the other hand, all current standardized communication systems employ some form of known inputs to allow channel estimation. The channel estimation performance in those cases can be optimized by a semiblind approach which exploits both training and blind information. When the inputs are colored and have sufficiently different spectra, the MIMO channel may become blindly identifiable up to one constant phase factor per input, and this under looser conditions on the channel. For the case of spatial multiplexing, possible cooperation between the channel inputs allows for more complex MIMO source prefiltering that may allow blind MIMO channel identification up to just one global constant phase factor. We introduce semiblind criteria that are motivated by the Gaussian ML approach. They combine a training based weighted least-squares criterion with a blind criterion based on linear prediction. A variety of blind criteria are considered for the various cases of source coloring