On the improved path metric for soft-input soft-output tree detection

In this paper, we propose a new path metric, which improves the performance of soft-input soft-output (SISO) tree detection for iterative detection and decoding (IDD) systems. While the conventional path metric accounts for the contribution of symbols on a visited path due to the causal nature of tree search, the new path metric, called improved path metric, re.ect the contribution of unvisited paths using an unconstrained minimum mean squared error (MMSE) estimate of undecided symbols. The improved path metric is applied to SISO M-algorithm, which finds a list of symbol candidates based on breadth-first search strategy and computes a posteriori probability of each entry of the symbol vector. We study the probability of correct path loss (CPL) for the improved path metric and confirm the performance improvement over the conventional path metric.