Per-survivor processing

Per-Survivor Processing (PSP) provides a general framework for the approximation of Maximum Likelihood Sequence Estimation (MLSE ) algorithms whenever the presence of unknown quantities prevents the precise use of the classical Viterbi Algorithm. The class of PSP decoding algorithms is based on the concept that data-aided estimation of unknown parameters maybe embedded into the structure of the Viterbi algorithm itself. Two general and overlapping areas for possible applications of PSP are (a) MLSE decoding in the presence of unknown channel parameters and (b) reduced-state MLSE decoding. Specific PSP-based approximations of MLSE decoding algorithms are presented for coded and uncoded transmission over time-varying and fading channels. In all the considered cases, it is found that PSP algorithms show negligible performance degradation with respect to ideal MLSE and outperform conventional techniques based on the use of tentative decisions.