Code-aided maximum-likelihood ambiguity resolution through free-energy minimization

  • Authors:
  • Cédric Herzet;Kampol Woradit;Henk Wymeersch;Luc Vandendorpe

  • Affiliations:
  • Centre Rennes-Bretagne Atlantique, Campus Universitaire de Beaulieu, Rennes, France;Electrical Engineering Department, Faculty of Engineering, Srinakharinwirot University, Nakonnayok, Thailand;Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden;Communications Laboratory, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2010

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Abstract

In digital communication receivers, ambiguities in terms of timing and phase need to be resolved prior to data detection. In the presence of powerful error-correcting codes, which operate in low signal-to-noise ratios (SNR), long training sequences are needed to achieve good performance. In this contribution, we develop a new class of code-aided ambiguity resolution algorithms, which require no training sequence and achieve good performance with reasonable complexity. In particular, we focus on algorithms that compute the maximum-likelihood (ML) solution (exactly or in good approximation) with a tractable complexity, using a factor-graph representation. The complexity of the proposed algorithm is discussed and reduced complexity variations, including stopping criteria and sequential implementation, are developed.