  Tree detection techniques are often used to reduce the complexity of a
posteriori probability (APP) detection in high dimensional multi-antenna
wireless communication systems. In this paper, we introduce an efficient
soft-input soft-output tree detection algorithm that employs a new type of
look-ahead path metric in the computation of its branch pruning (or sorting).
While conventional path metrics depend only on symbols on a visited path, the
new path metric accounts for unvisited parts of the tree in advance through an
unconstrained linear estimator and adds a bias term that reflects the
contribution of as-yet undecided symbols. By applying the linear estimate-based
look-ahead path metric to an M-algorithm that selects the best M paths for each
level of the tree we develop a new soft-input soft-output tree detector, called
an improved soft-input soft-output M-algorithm (ISS-MA). Based on an analysis
of the probability of correct path loss, we show that the improved path metric
offers substantial performance gain over the conventional path metric. We also
demonstrate through simulations that the ISS-MA provides a better
performance-complexity trade-off than existing soft-input soft-output detection
algorithms.
