Asymptotic efficiency of a blind maximum likelihood sequence detector

Jill K. Nelson, Andrew C. Singer

Research output: Contribution to journalConference article

Abstract

We consider the performance of blind maximum likelihood sequence detection (MLSD) when the recursive least-squares (RLS) algorithm is used to update channel estimates. We employ asymptotic efficiency analysis to characterize the performance of the detector as the signal-to-noise ratio (SNR) approaches infinity. Asymptotic efficiency analysis allows us to quantify the loss in performance due to the presence of inter-symbol interference (ISI) and the lack of channel knowledge. We show that, under certain conditions, the asymptotic efficiency of the detector depends only on a single most-likely noise realization. Our results indicate that the performance of the RLS-based detector is strongly dependent on both the magnitude of the ISI and the number of data samples available.

Original languageEnglish (US)
Pages (from-to)1667-1671
Number of pages5
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume2
StatePublished - Dec 1 2003
EventConference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 9 2003Nov 12 2003

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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