Decision directed algorithms for multiuser detection

G. V. Moustakides

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


We present a class of constraint LMS like adaptive linear detection schemes that constitutes a generalization to the popular blind adaptive detector. We show that, contrary to the general belief, the conventional LMS and its constraint version, when in training mode, do not necessarily outperform the blind LMS of [1]. Trained algorithms uniformly outperform their blind counterparts only if they incorporate knowledge of the amplitude of the user of interest. Decision directed versions of such algorithms are shown to be equally efficient as their trained prototypes and significantly better than the blind versions.

Original languageEnglish (US)
Pages (from-to)8
Number of pages1
JournalIEEE International Symposium on Information Theory - Proceedings
StatePublished - 2001
Externally publishedYes
Event2001 IEEE International Symposium on Information Theory (ISIT 2001) - Washington, DC, United States
Duration: Jun 24 2001Jun 29 2001

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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