ASYMPTOTICALLY OPTIMAL BLIND FRACTIONALLY-SPACED CHANNEL ESTIMATION AND PERFORMANCE ANALYSIS

When the received data are fractionally sampled, the magnitude and pha se of most linear time-invariant FIR communications channels can be es timated from second-order output-only statistics, We present a general cyclic correlation matching algorithm for known order FIR blind chann el identification that has closed-form expressions for calculating the asymptotic variance of the channel estimates. We show that for a part icular choice of weights, the weighted matching estimator yields (at l east for large samples) the minimum variance channel estimator among a ll unbiased estimators based on second-order statistics, Furthermore, the matching approach, unlike existing methods, provides a useful esti mate even when the channel is not uniquely identifiable from second-or der statistics.