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

C. Herzet ; K. Woradit ; Henk Wymeersch (Institutionen för signaler och system, Kommunikationssystem) ; L. Vandendorpe
IEEE Transactions on Signal Processing (1053-587X). Vol. 58 (2010), 12, p. 6238-6250.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Belief propagation, maximum-likelihood estimation, optimal receivers, frame synchronization, node synchronization, belief propagation, awgn, channels, factor graphs, turbo-codes, sum-product, algorithm, recovery, systems



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Denna post skapades 2010-12-16. Senast ändrad 2016-12-06.
CPL Pubid: 131097

 

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