Efficient Soft-Input Soft-Output Tree Detection Via an Improved Path Metric

TL;DR

This paper presents an improved soft-input soft-output tree detection algorithm for multi-antenna wireless systems, utilizing a novel look-ahead path metric that enhances performance and reduces complexity.

Contribution

It introduces a new look-ahead path metric based on linear estimation, improving detection accuracy and efficiency in soft-input soft-output tree detection algorithms.

Findings

Significant performance gain over conventional metrics.

Better performance-complexity trade-off demonstrated through simulations.

Enhanced detection accuracy in high-dimensional systems.

Abstract

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.