Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization
Artikel i vetenskaplig tidskrift, 2010

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.

channels

sum-product

awgn

algorithm

belief propagation

recovery

optimal receivers

maximum-likelihood estimation

frame synchronization

Belief propagation

turbo-codes

factor graphs

systems

node synchronization

Författare

C. Herzet

INRIA Institut National de Recherche en Informatique et en Automatique

K. Woradit

Srinakharinwirot University

Henk Wymeersch

Chalmers, Signaler och system, Kommunikations- och antennsystem, Kommunikationssystem

Luc Vandendorpe

Universite catholique de Louvain

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 58 12 6238-6250 5551242

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Elektroteknik och elektronik

DOI

10.1109/TSP.2010.2068291

Mer information

Senast uppdaterat

2018-03-08