A Tree Search Method for Iterative Decoding of Underdetermined Multiuser Systems

TL;DR

This paper introduces a novel tree search method for iterative decoding in underdetermined multiuser systems, improving performance over existing linear filtering and list detection approaches.

Contribution

The paper proposes a transformation enabling standard tree search algorithms to be applied to underdetermined systems, enhancing decoding efficiency and accuracy.

Findings

Tree search method outperforms existing approaches

Applicable to underdetermined multiuser systems

Improves decoding performance

Abstract

Application of the turbo principle to multiuser decoding results in an exchange of probability distributions between two sets of constraints. Firstly, constraints imposed by the multiple-access channel, and secondly, individual constraints imposed by each users' error control code. A-posteriori probability computation for the first set of constraints is prohibitively complex for all but a small number of users. Several lower complexity approaches have been proposed in the literature. One class of methods is based on linear filtering (e.g. LMMSE). A more recent approach is to compute approximations to the posterior probabilities by marginalising over a subset of sequences (list detection). Most of the list detection methods are restricted to non-singular systems. In this paper, we introduce a transformation that permits application of standard tree-search methods to underdetermined systems. We find that the resulting tree-search based receiver outperforms existing methods.