Blind zero-forcing equalization without channel estimation

We present methods for computing fractionally spaced zero-forcing blind equalizers with arbitrary delay directly from second order statistics of the observations without any channel identification. Such direct estimation, without even partial channel identification, completely avoids channel identification errors, resulting in better equalization performance. 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.