Efficient soft-input soft-output tree detection via an improved path metric

Jun Won Choi, Byonghyo Shim, Andrew C. Singer

Research output: Contribution to journalArticlepeer-review


Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in multiantenna 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 process of 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 proposed ISS-MA can be a promising candidate for soft-input soft-output detection in high-dimensional systems.

Original languageEnglish (US)
Article number6157084
Pages (from-to)1518-1533
Number of pages16
JournalIEEE Transactions on Information Theory
Issue number3
StatePublished - Mar 2012


  • Iterative detection and decoding (IDD)
  • M-algorithm
  • k-best search
  • list sphere decoding
  • look-ahead path metric
  • soft-input soft-output detection
  • tree detection
  • turbo principle

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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