A constrained decision feedback equalizer for reduced complexity maximum likelihood sequence estimation

Wen-Rong Wu*, Yih Ming Tsuie

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

The maximum likelihood sequence estimator (MLSE) is usually implemented by the Viterbi algorithm (VA). The computational complexity of the VA grows exponentially with the length of the channel response. With some performance reduction, a decision-feedback equalizer (DFE) can be used to shorten the channel response. This greatly reduces the computational requirement for the VA. However, for many real-world applications, the complexity of the DFE/MLSE approach may be still too high. In this paper, we propose a constrained DFE that offers much lower VA computational complexity. The basic idea is to pose some constraints on the DFE such that the postcursors of the shortened channel response have only discrete values. As a result, the multiplication operations can be replaced by shift operations making the VA almost multiplication free. This will greatly facilitate the real world applications of the MLSE algorithm. Simulation results show that while the proposed algorithm basically offers the same performance as the original MLSE performance, the VA is much more efficient than the conventional approach.

Original languageEnglish
Pages (from-to)231-238
Number of pages8
JournalIEICE Transactions on Communications
VolumeE85-B
Issue number1
StatePublished - 1 Jan 2002

Keywords

  • DFE
  • MLSE
  • Multiplication operations
  • Shift operations
  • VA

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