Equalization of sparse intersymbol-interference channels revisited

Abstract

Sparse intersymbol-interference (ISI) channels are encountered in a variety of communication systems, especially in high-datarate systems. These channels have a large memory length, but only a small number of significant channel coefficients. In this paper, equalization of sparse ISI channels is revisited with focus on trellis-based techniques. Due to the large channel memory length, the complexity of maximum-likelihood sequence estimation by means of the Viterbi algorithm is normally prohibitive. In the first part of the paper, a unified framework based on factor graphs is presented for complexity reduction without loss of optimality. In this new context, two known reduced-complexity trellis-based techniques are recapitulated. In the second part of the paper a simple alternative approach is investigated to tackle general sparse ISI channels. It is shown that the use of a linear filter at the receiver renders the application of standard reduced-state trellis-based equalization techniques feasible without significant loss of optimality.