Channel robust blind fractionally-spaced equalization

We present a new adaptive robust equalization method aimed to overcome the issue of lack of disparity of most recent spatio-temporal diversity based equalization algorithms, and to pick the equalizer minimizing input/output errors in the presence of additive noise. It combines fractionally-spaced equalization by constant modulus algorithm (FSE-CMA) robustness to lack of disparity and delay diversity to find several equalizer settings in different basins of attraction of the FSE-CM cost-function. We prove that the algorithm allows one to choose the best equalizer's output when there is disparity. Moreover, in the worst possible channel case, i.e. when there is such a lack of disparity that even the FSE-CMA fails, some of the equalizers may escape from converging to undesired settings and come very close to MMSE.