Blind multiuser detection: A subspace approach

Xiaodong Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

669 Scopus citations


A new multiuser detection scheme based on signal subspace estimation is proposed. It is shown that under this scheme, both the decorrelating detector and the linear minimum-mean-square-error (MMSE) detector can be obtained blindly, i.e., they can be estimated from the received signal with the prior knowledge of only the signature waveform and timing of the user of interest. The consistency and asymptotic variance of the estimates of the two linear detectors are examined. A blind adaptive implementation based on a signal subspace tracking algorithm is also developed. It is seen that compared with the previous minimum-output-energy blind adaptive multiuser detector, the proposed subspace-based blind adaptive detector offers lower computational complexity, better performance, and robustness against signature waveform mismatch. Two extensions are made within the framework of signal subspace estimation. First, a blind adaptive method is developed for estimating the effective user signature waveform in the multipath channel. Secondly, a multiuser detection scheme using spatial diversity in the form of an antenna array is considered. A blind adaptive technique for estimating the array response for diversity combining is proposed. It is seen that under the proposed subspace approach, blind adaptive channel estimation and blind adaptive array response estimation can be integrated with blind adaptive multiuser detection, with little attendant increase in complexity.

Original languageEnglish (US)
Pages (from-to)677-690
Number of pages14
JournalIEEE Transactions on Information Theory
Issue number2
StatePublished - 1998

All Science Journal Classification (ASJC) codes

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


  • Array response estimation
  • Blind adaptation
  • Channel estimation
  • Linear multiuser detection
  • Subspace tracking


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