Direct estimation of blind zero-forcing equalizers based on second-order statistics

Most existing zero-forcing equalization algorithms rely either on higher th an second-order statistics or on partial or complete channel identification . We describe methods for computing fractionally spaced zero-forcing blind equalizers with arbitrary delay directly from second-order statistics of th e observations without channel identification. We first develop a batch-typ e algorithm; then, adaptive algorithms are obtained by linear prediction an d gradient descent optimization. Our adaptive algorithms do not require cha nnel order estimation, nor rank estimation. Compared with other second-orde r statistics-based approaches, ours do not require channel identification a t all, On the other hand, compared with the CMA-type algorithms, ours use o nly second-order statistics; thus, no local convergence problem exists, and faster convergence can be achieved, Simulations show that our algorithms o utperform most typical existing algorithms.