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.
systems
algorithm
belief propagation
recovery
node synchronization
frame synchronization
factor graphs
optimal receivers
awgn
maximum-likelihood estimation
turbo-codes
Belief propagation
channels
sum-product
Author
C. Herzet
Institut National de Recherche en Informatique et en Automatique (INRIA)
K. Woradit
Srinakharinwirot University
Henk Wymeersch
Chalmers, Signals and Systems, Communication, Antennas and Optical Networks
Luc Vandendorpe
Universite catholique de Louvain
IEEE Transactions on Signal Processing
1053-587X (ISSN) 1941-0476 (eISSN)
Vol. 58 12 6238-6250 5551242Areas of Advance
Information and Communication Technology
Subject Categories
Electrical Engineering, Electronic Engineering, Information Engineering
DOI
10.1109/TSP.2010.2068291