Blind channel estimation by least squares smoothing

A linear least squares smoothing approach is proposed for the blind channel estimation. It is shown that the single-input multiple-output moving average process has the property that the error sequence of the least squares smoother, under certain conditions, uniquely determines the channel impulse response. The relationship among the dimension of the observation space, channel order and smoothing delay is presented. A new algorithm for channel estimation based on the least squares smoothing is developed. The proposed approach has the finite-sample convergence property in the absence of the channel noise. It also has a structure suitable for recursive implementations