Reduced-state sequence estimation for trellis-coded modulation on intersymbol interference channels

The authors consider the detection of trellis codes designed for ideal channels when they operate in the presence of intersymbol interference (ISI). A well-structured reduced-state sequence-estimation (RSSE) algorithm is described which can achieve the performance of maximum-likelihood sequence estimation (MLSE) at drastically reduced complexity. The authors start by constructing proper reduced-state trellises by merging the states of the ML supertrellis using set-partitioning principles. Then the Viterbi algorithm (VA) searches these trellises using a modified ML metric. The authors examine the performance and complexity of RSSE and compare it to that of MLSE and DFE (decision-feedback equalization). They also discuss the application of RSSE to higher dimensional trellis codes and to trellis coding with interleaving.