Soft-input soft-output list sphere detection with a probabilistic radius tightening

Jaeseok Lee, Byonghyo Shim, Insung Kang

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


In this paper, we present a low-complexity list sphere detection algorithm for achieving near-optimal a posteriori probability (APP) detection in an iterative detection and decoding (IDD). Motivated by the fact that the list sphere decoding searching a fixed number of candidates is computationally inefficient in many scenarios, we design a criterion to search lattice points with non-vanishing likelihood and then derive a hypersphere radius satisfying this condition. Further, in order to exploit the original sphere constraint as it is instead of using necessary conditioned version, we combine a probabilistic tree pruning strategy and the proposed list sphere search. Two features, tightened hypersphere radius and probabilistic tree pruning, collaborate and improve the search efficiency in a complementary fashion. Through simulations on 4x4 MIMO system, we show that the proposed method provides substantial reduction in complexity while achieving negligible performance loss over the conventional list sphere detection.

Original languageEnglish
Article number6213035
Pages (from-to)2848-2857
Number of pages10
JournalIEEE Transactions on Wireless Communications
Issue number8
Publication statusPublished - 2012


  • Iterative detection and decoding
  • Sphere decoding
  • a posteriori probability
  • complexity reduction
  • multiple-input multiple-output system
  • probabilistic radius tightening

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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