Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization


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.

DOI: 10.1109/TSP.2010.2068291

Extracted Key Phrases

10 Figures and Tables

Cite this paper

@article{Herzet2010CodeAidedMA, title={Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization}, author={C{\'e}dric Herzet and Kampol Woradit and Henk Wymeersch and Luc Vandendorpe}, journal={IEEE Transactions on Signal Processing}, year={2010}, volume={58}, pages={6238-6250} }