Soft-decision-based node synchronization for Viterbi decoders

Mengali, U.; Pellizzoni, R.; Spalvieri, A.

doi:10.1109/26.412728

Cited by 12 publications

(11 citation statements)

References 5 publications

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“…[5], [6], [7]. In these contributions, the authors consider (although with a certain degree of suboptimality) the CA ambiguity-resolution problem in the context of MAP (or ML) estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In these contributions, the authors consider (although with a certain degree of suboptimality) the CA ambiguity-resolution problem in the context of MAP (or ML) estimation. In [5], the authors modify the likelihood function by only keeping its largest term and decide whether the current estimate is true or not by a threshold decision on this modified likelihood function. In [6], another approach is followed: the authors place the MAP estimation problem into the framework of the expectation-maximization (EM) algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Code-Aided ML Ambiguity Resolution

Herzet

Vandendorpe

2007

2007 IEEE International Conference on Communications

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This paper deals with code-aided (CA) maximumlikelihood (ML) phase and timing ambiguity resolution. We propose a methodology based on the sum-product algorithm (SPA) to exactly solve this problem with a tractable complexity. In particular, we emphasize that the proposed ML ambiguityresolution algorithm has a complexity which is at most equal to the complexity of recently-proposed powerful ML-like ambiguityresolution methods. Finally, we compare through simulation results the ability of CA and conventional data-aided methods to resolve phase ambiguities.

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“…[5], [6], [7]. In these contributions, the authors consider (although with a certain degree of suboptimality) the CA ambiguity-resolution problem in the context of MAP (or ML) estimation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Code-Aided ML Ambiguity Resolution

Herzet

Vandendorpe

2007

2007 IEEE International Conference on Communications

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“…The ML criterion is the core of a number of ambiguity-resolution methods proposed in the literature, such as [20]- [22]. In these contributions, the ML solution is assumed to be intractable because the evaluation of the likelihood function p R|B (r|b) requires a summation over all possible sequences, i.e.,…”

Section: B Data Detection and Ambiguity Resolutionmentioning

confidence: 99%

“…In the latter class of algorithms, [20] modifies the likelihood function by only keeping its largest term and decides whether the current estimate is true or not by a threshold decision on this modified likelihood function. Another approach is followed in [21]: the authors place the MAP estimation problem into the framework of the expectation-maximization (EM) algorithm [23].…”

Section: Introductionmentioning

confidence: 99%

“…Among CA ambiguity-resolution methods, one can distinguish between two main approaches: the authors either propose ad-hoc algorithms based on the fact that some decoder metrics vary as a function of the considered hypotheses [15]- [19], or place the CA ambiguity-resolution problem in the context of maximum a posteriori (MAP) or ML estimation, [20]- [22]. In the latter class of algorithms, [20] modifies the likelihood function by only keeping its largest term and decides whether the current estimate is true or not by a threshold decision on this modified likelihood function.…”

Section: Introductionmentioning

confidence: 99%

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

Herzet

Woradit

Wymeersch

et al. 2010

IEEE Trans. Signal Process.

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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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Code-Aided ML Joint Delay Estimation and Frame Synchronization

Wymeersch

Moeneclaey

Multimedia Systems and Applications Series

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Soft-decision-based node synchronization for Viterbi decoders

Cited by 12 publications

References 5 publications

Code-Aided ML Ambiguity Resolution

Code-Aided ML Ambiguity Resolution

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

Code-Aided ML Joint Delay Estimation and Frame Synchronization

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