Direct blind MMSE channel equalization based on second-order statistics

A family of new MMSE blind channel equalization algorithms based on second- order statistics are proposed. Instead of estimating the channel impulse re sponse, we directly estimate the cross-correlation function needed in Wiene r-Hopf filters. We develop several different schemes to estimate the cross- correlation vector, with which different Wiener filters are derived accordi ng to minimum mean square error (MMSE). Unlike many known subspace methods, these equalization algorithms do not rely on signal and noise subspace sep aration and are consequently more robust to channel order estimation errors . Their implementation requires no adjustment for either single- or multipl e-user systems, They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) sys tems into a memoryless signal mixing system for source separation. The impl ementations of these algorithms on SIMO system are given, and simulation ex amples are provided to demonstrate their superior performance over some exi sting algorithms.