Soft quasi-maximum-likelihood detection for multiple-antenna channels

Baldur Steingrimsson, Zhi Quan Luo, Kon Max Wong

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


The paper addresses soft maximum-likelihood (ML) detection for multiple-antenna wireless channels. We propose a soft quasi-ML detector which maximizes the log-likelihood function by deploying a semi-definite relaxation (SDR). Given perfect channel state information at the receiver, the quasi-ML SDR detector achieves the performance of the optimal ML detector in both coded and uncoded multiple-input, multiple-output (MIMO) channels with quadrature phase-shift keying modulation and frequency-flat Rayleigh fading. The complexity of the quasi-ML SDR detector is much less than that of the optimal ML detector, and, thus, the quasi-ML detector offers more favorable performance/complexity trade-off. Compared to the existing sphere decoder the quasi-ML detector enjoys low polynomial worst-case complexity, as well as guaranteed near capacity performance.

Original languageEnglish (US)
Pages (from-to)2330-2334
Number of pages5
JournalIEEE International Conference on Communications
StatePublished - Jul 18 2003
Event2003 International Conference on Communications (ICC 2003) - Anchorage, AK, United States
Duration: May 11 2003May 15 2003


  • ML detection
  • Quasi-ML detection
  • Semi-definite relaxation
  • Soft channel decoding


Dive into the research topics of 'Soft quasi-maximum-likelihood detection for multiple-antenna channels'. Together they form a unique fingerprint.

Cite this