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Phase Estimation and Phase Ambiguity Resolution by Message Passing

  • Justin Dauwels
  • Henk Wymeersch
  • Hans-Andrea Loeliger
  • Marc Moeneclaey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3124)

Abstract

Several code-aided algorithms for phase estimation have recently been proposed. While some of them are ad-hoc, others are derived in a systematic way. The latter can be divided into two different classes: phase estimators derived from the expectation-maximization (EM) principle and estimators that are approximations of the sum-product message passing algorithm. In this paper, the main differences and similarities between these two classes of phase estimation algorithms are outlined and their performance and complexity is compared. Furthermore, an alternative criterion for phase ambiguity resolution is presented and compared to an EM based approach proposed earlier.

Keywords

Message Passing Phase Estimation LDPC Code Factor Graph Phase Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Justin Dauwels
    • 1
  • Henk Wymeersch
    • 2
  • Hans-Andrea Loeliger
    • 1
  • Marc Moeneclaey
    • 2
  1. 1.Dept. of Information Technology and Electrical EngineeringETHZürichSwitzerland
  2. 2.DIGCOM Research Group, TELIN Dept.Ghent UniversityGentBelgium

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