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Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization

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.
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Submitted on : Monday, January 9, 2012 - 5:32:14 PM
Last modification on : Thursday, January 20, 2022 - 5:28:29 PM
Long-term archiving on: : Tuesday, April 10, 2012 - 2:20:34 AM

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Cédric Herzet, Woradit Kampol, Henk Wymeersch, Luc Vandendorpe. Code-aided Maximum-likelihood Ambiguity Resolution Through Free-energy Minimization. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2010. ⟨inria-00589353⟩

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