QR factorization based blind channel identification and equalization with second-order statistics. (Q2734272)

Two QR-based algorithms for blind channel identification and equalization are presented in the paper. The first algorithm is a batch algorithm and is the same kind as the algorithm developed on the basis of second-order statistics, but it uses QR decomposition of the data matrix instead of singular value decomposition.NEWLINENEWLINEThe second algorithm uses the updating of a rank-revealing decomposition and is recursive. Its computational complexity is lower, and it is computationally more efficient. Also it preserves the fast convergence property of the subspace algorithms. It does not need estimation of the noise variance. Computer simulations are used to study the performance of this algorithm. It is shown that it is more robust when channels are ill-conditioned. Also it is shown that the proposed algorithm can effectively track channel variations with convergence faster than other adaptive algorithms, such as the fractionally spaced super-exponential algorithm.