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

sum-product

awgn

algorithm

belief propagation

recovery

optimal receivers

maximum-likelihood estimation

frame synchronization

Belief propagation

turbo-codes

factor graphs

systems

node synchronization

Author

C. Herzet

INRIA Institut National de Recherche en Informatique et en Automatique

K. Woradit

Srinakharinwirot University

Henk Wymeersch

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Luc Vandendorpe

Universite catholique de Louvain

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 58 12 6238-6250 5551242

Areas of Advance

Information and Communication Technology

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TSP.2010.2068291

More information

Latest update

3/8/2018 1