Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization
(2010) IEEE Transactions on Signal Processing —
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- Authors
- Abstract
- 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.
- Affiliations
INRIA Centre Rennes Bretagne Atlantique, Campus universitaire de Beaulieu, Rennes, France Srinakharinwirot University, Nakonnayok, Thailand EE dpt Chalmers University of Technology, Gothenburg, Sweden Department of Signals and Systems,
Citations
Herzet, C., Woradit, K., Wymeersch, H., & Vandendorpe, L. (2010). Code-Aided Maximum-Likelihood Ambiguity Resolution Through Free-Energy Minimization. IEEE Transactions on Signal Processing. https://doi.org/10.1109/TSP.2010.2068291