Phase-Ambiguity Resolution in a Four-Phase PSK Communications System

Cacciamani, E. R.; Wolejsza, C. J.

doi:10.1109/tcom.1971.1090761

Cited by 17 publications

(4 citation statements)

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“…In this section, the BER performance of the data-aided (DA) [1]- [3], the expectation-maximization (EM) [21], and the free energy minimization (FE) algorithms are evaluated and compared to that of the perfect synchronization, i.e., to the case where the decoder knows perfectly timing and phase ambiguities.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Conventional methods dealing with ambiguity resolution operate in Data-aided (DA) mode, i.e., making use of pilot symbols to make a decision. We mention the contributions of Massey [1] and Lui et al [2] on timing ambiguity resolution, 1 and Cacciamani et al [3] on phase ambiguity resolution, in which maximum-likelihood (ML) DA algorithms are derived.…”

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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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Herzet

Woradit

Wymeersch

et al. 2010

IEEE Trans. Signal Process.

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show abstract

“…Conventional methods dealing with ambiguity resolution operate in Dataaided (DA) mode, i.e., make use of pilot symbols to make their decision. We mention the contributions of Massey [1], Lui et al [2] and Cacciamani et al [3], in which maximumlikelihood (ML) DA algorithms are derived for phase and timing ambiguity resolution, respectively.…”

Section: Introductionmentioning

confidence: 99%

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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“…When phase estimation (PE) is performed by means of a conventional non-dataaided (NDA) algorithm [1], the resulting estimate exhibits a phase ambiguity, due to the rotational symmetries of the signalling constellation. Phase ambiguity resolution (PAR) can be accomplished by a data-aided (DA) algorithm that exploits the presence of a known pilot sequence in the transmitted data stream [2]. The need for PAR can be removed by using differential encoding, which however results in a BER degradation, and requires significant changes to the decoder in case of iterative demodulation/decoding [3].…”

Section: Introductionmentioning

confidence: 99%

Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution

Wymeersch

Moeneclaey

2005

EURASIP J. Adv. Signal Process.

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As many coded systems operate at very low signal-to-noise ratios, synchronization becomes a very difficult task. In many cases, conventional algorithms will either require long training sequences or result in large BER degradations. By exploiting code properties, these problems can be avoided. In this contribution, we present several iterative maximum-likelihood (ML) algorithms for joint carrier phase estimation and ambiguity resolution. These algorithms operate on coded signals by accepting soft information from the MAP decoder. Issues of convergence and initialization are addressed in detail. Simulation results are presented for turbo codes, and are compared to performance results of conventional algorithms. Performance comparisons are carried out in terms of BER performance and mean square estimation error (MSEE). We show that the proposed algorithm reduces the MSEE and, more importantly, the BER degradation. Additionally, phase ambiguity resolution can be performed without resorting to a pilot sequence, thus improving the spectral efficiency.

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Phase-Ambiguity Resolution in a Four-Phase PSK Communications System

Cited by 17 publications

References 1 publication

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

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

Code-Aided ML Ambiguity Resolution

Iterative Code-Aided ML Phase Estimation and Phase Ambiguity Resolution

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