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

Most existing zero-forcing equalization algorithms rely either on higher than 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 the observations without channel identification. We first develop a batch-type algorithm; then, adaptive algorithms are obtained by linear prediction and gradient descent optimization. Our adaptive algorithms do not require channel order estimation, nor rank estimation. Compared with other second-order statistics-based approaches, ours do not require channel identification at all. On the other hand, compared with the CMA-type algorithms, ours use only second-order statistics; thus, no local convergence problem exists, and faster convergence can be achieved. Simulations show that our algorithms outperform most typical existing algorithms.