Low-complexity decoding via reduced dimension maximum-likelihood search

Jun Won Choi, Byonghyo Shim, Andrew C. Singer, Nam Ik Cho

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i.e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) 10-1 ∼ 10-4 while limiting performance loss to within 1 dB from ML detection.

Original languageEnglish
Pages (from-to)1780-1793
Number of pages14
JournalIEEE Transactions on Signal Processing
Issue number3 PART 2
Publication statusPublished - 2010 Mar


  • Dimension reduction
  • List tree search
  • Maximumlikelihood (ML) decoding
  • Minimum mean square error (MMSE)
  • Multiple input multiple output (MIMO)
  • Sphere decoding
  • Stack algorithm

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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