Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization
Journal article, 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
maximum-likelihood estimation
optimal receivers
belief propagation
Belief propagation
systems
factor graphs
frame synchronization
sum-product
recovery
awgn
algorithm
node synchronization
turbo-codes
Author
C. Herzet
INRIA Institut National de Recherche en Informatique et en Automatique
K. Woradit
Srinakharinwirot University
Henk Wymeersch
Chalmers, Signals and Systems, Kommunikationssystem, informationsteori och antenner
Luc Vandendorpe
Universite Catholique de Louvain
IEEE Transactions on Signal Processing
1053-587X (ISSN)
Vol. 58 12 6238-6250 5551242Areas of Advance
Information and Communication Technology
Subject Categories (SSIF 2011)
Electrical Engineering, Electronic Engineering, Information Engineering
DOI
10.1109/TSP.2010.2068291