Linear prediction methods for blind fractionally spaced equalization

We describe adaptive methods for estimating FIR zero-forcing blind equalizers with arbitrary delay directly from the linear predictions of the observations. While most current methods require inversion or singular value decomposition (SVD) of the correlation matrix, our methods need only to solve two linear prediction problems. They can be implemented as RLS or LMS algorithms to recursively update the equalizer estimation. they are computationally efficient. The computational complexity in each recursion can be less than 15(LN)² in the RLS case, where LN equals the equalizer length, and 3L(LN) in the LMS case, where L is the number of subchannels. The performance of the proposed methods and comparisons with existing approaches are shown by simulation to demonstrate their effectiveness