Adaptive noise-predictive maximum likelihood detection using tentative decision and partial path selection

Abstract

We present a noise-predictive maximum likelihood (NPML) detection scheme considering both low complexity and effective adaptation. First, for achieving low complexity, we exploit the modified Viterbi decoding method that partially selects the survival paths. The partial path selection method limits the number of selected paths among all survival paths at the Viterbi trellis and selects a path with minimum metric among the selected paths while the original Viterbi algorithm considers all paths and decides the best path. Next, for effective adaptation, we propose an adaptive NPML scheme exploiting a tentative decision value of the Viterbi decoding process.