On a whitening approach to partial channel estimation and blind equalization of FIR/IIR multiple-input multiple-output channels

Channel estimation and blind equalization of multiple-input multiple-output (MIMO) communications channels is considered using primarily the second-or der statistics of the data. Such models arise when a single receiver data f rom multiple sources is fractionally sampled (assuming that there is excess bandwidth) or when an antenna array is used with or without fractional sam pling. We consider estimation of (partial) channel impulse response and des ign of finite-length minimum mean-square error (MMSE) blind equalizers. The basis of the approach is the design of a zero-forcing equalizer that white ns the noise-free data. We allow infinite impulse response (IIR) channels, Moreover, the multichannel transfer function need not be column reduced. Ou r approaches also work when the "subchannel" transfer functions have common zeros as long as the common zeros are minimum-phase zeros. The channel len gth or model orders need not be known. The sources are recovered up to a un itary mixing matrix and are further "unmixed" using higher order statistics of the data. A linear prediction approach is also considered under the abo ve conditions of possibly IIR channels, common subchannel zeros/factors, an d not-necessarily column-reduced channels. Four illustrative simulation exa mples are provided.