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

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.
Type de document :
Article dans une revue
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2010
Liste complète des métadonnées

Littérature citée [39 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00589353
Contributeur : Cedric Herzet <>
Soumis le : lundi 9 janvier 2012 - 17:32:14
Dernière modification le : mercredi 11 avril 2018 - 01:57:37
Document(s) archivé(s) le : mardi 10 avril 2012 - 02:20:34

Fichier

manuscript.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00589353, version 1

Collections

Citation

Cedric Herzet, Woradit Kampol, Henk Wymeersch, Luc Vandendorpe. Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2010. 〈inria-00589353〉

Partager

Métriques

Consultations de la notice

145

Téléchargements de fichiers

150