Least squares approach to ZF blind equalization using multistep prediction

Blind equalization of transmission channels is important in communications areas and signal processing applications because it does not need a training sequence, nor does it require a priori channel information. Recently, Tong et al., (1994, 1995), proposed solutions for this problem by exploiting the diversity induced by antenna array or time oversampling, leading to the second order statistics techniques, for example, subspace method, prediction error method, and so on. The linear prediction error method is perhaps the most attractive in practice due to its being insensitive to blind equalizer length mismatch as well as for its simple adaptive filter implementation. Unfortunately, the previous one-step ahead prediction error method is known to be limited in arbitrary delay. In this paper, we induce the optimal delay, and propose an adaptive blind equalizer with multistep linear prediction using the RLS- and LMS-type algorithm. Simulation results are presented to demonstrate the proposed algorithm and to compare it with existing algorithms.