Maximum-likelihood sequence estimation from subbands

Abstract

In recent papers by the first author [1, 2], it has been shown that Subband Decomposition results in a reduction of memory in the subbands. Furthermore, the total prediction error power from the subbands is less than the fullband prediction error, for finite orders of prediction. In this paper we apply these results to equalizer design using subbands. It is shown that Maximum-Likelihood (ML) Sequence Estimation in the subbands offers a gain in terms of estimation error. In addition, there is a considerable reduction in computational complexity. It is also demonstrated that by working in the subband domain, it is possible to avoid the problems associated with the presence of nulls in the channel frequency response.