Iterative maximum-likelihood trellis decoding for block codes

An iterative trellis search technique is described for the maximum-likeliho od (ML) soft decision decoding of block codes. The proposed technique deriv es its motivation from the fact that a given block code may be a subcode fo r a parent code whose associated trellis has substantially fewer edges. Thr ough the use of list-Viterbi decoding and an iterative algorithm, the propo sed technique allows for the use of a trellis for the parent code in the ML decoding of the desired subcode, Complexity and performance analyses, as w ell as details of potential implementations, indicate a substantial reducti on in decoding complexity for linear block codes of practical length while achieving ML or near-ML soft decision performance.