Blind channel identification by subspace tracking and successive cancellation

Abstract

Traditional subspace methods (SS) for blind channel identification require accurate rank estimation with a computational complexity of O(m/sup 3/), where m is the data vector length. We introduce new adaptive subspace algorithms using ULV updating and successive cancellation techniques. In addition to reducing the computational complexity to O(m/sup 2/), the new algorithms do not need to estimate the subspace rank. Channel length can be overestimated during the subspace tracking and channel vector optimization steps. It can then be recovered at the end by a successive cancellation procedure. Simulation shows that the new algorithms outperform the traditional SS methods for the case that the subspace rank is difficult to estimate.