Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization

Cédric Herzet (1) , Woradit Kampol (2) , Henk Wymeersch (3) , Luc Vandendorpe (4)

1 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
2 Electrical Engineering Department [Srinakharinwirot University]
3 Chalmers University of Technology [Göteborg]
4 TELE - Laboratoire de Télécommunications et Télédétection [Louvain]

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Cédric Herzet, Woradit Kampol, Henk Wymeersch, Luc Vandendorpe. Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization. IEEE Transactions on Signal Processing, 2010. ⟨inria-00589353⟩