Unified design of iterative receivers using factor graphs

Worthen, Andrew P.; Stark, W.E.

doi:10.1109/18.910595

Cited by 194 publications

(127 citation statements)

References 33 publications

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“…In the previous section, we emphasized that the minimum constrained Bethe free energy can be evaluated from a subset of the belief normalization factors, see e.g, (27) and (30). This results can be applied to the problem of phase and timing ambiguity resolution.…”

Section: E Implementation and Complexitymentioning

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: E Implementation and Complexitymentioning

confidence: 99%

“…Algorithms based on the minimization of the constrained Bethe free energy have also been proposed in the literature. We refer the reader to the following contributions [30]- [32].…”

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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“…The estimate of minimizing the MSE is given by (14) From the ISI channel model (1) it follows that (14) is given by (15) where is the submatrix of the system matrix and i.e., is the th column of . The submatrix is given by the equation at the bottom of the page for the time indices .…”

Section: E Siso Equalization Based On Linear Mmse Estimationmentioning

confidence: 99%

Turbo Equalization: An Overview

Tüchler

Singer

2011

IEEE Trans. Inform. Theory

177

160

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Abstract-Turbo codes and the iterative algorithm for decoding them sparked a new era in the theory and practice of error control codes. Turbo equalization followed as a natural extension to this development, as an iterative technique for detection and decoding of data that has been both protected with forward error correction and transmitted over a channel with intersymbol interference (ISI). In this paper, we review the turbo equalization approach to coded data transmission over ISI channels, with an emphasis on the basic ideas, some of the practical details, and many of the research directions that have arisen from this offshoot, introduced by Douillard, et al. of the original turbo decoding algorithm. The subsequent relaxation of the maximum a posteriori (MAP) equalization algorithm to include linear and other simpler receivers sparked a decade and a half of research into iterative algorithms, spanning research problems ranging from trellis coded modulation to underwater acoustic communications.Index Terms-Equalization, iterative decoding, minimum mean square error, turbo equalization.

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“…Factor graph and related algorithms, such as the sum-product algorithm (SPA) and the GMP principle, have been under extensive investigation in recent years [9]- [11]. Typical applications can be found in, e.g., [12]- [15] and references therein.…”

Section: Introductionmentioning

confidence: 99%

A factor-graph based ZP-OFDM receiver for deep water acoustic channels

Wang

Zhou

Catipovic

et al. 2010

Oceans 2010 MTS/Ieee Seattle

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Abstract-In this paper, we present a factor-graph based receiver for zero-padded orthogonal frequency division multiplexing (ZP-OFDM) transmissions over deep water channels with extremely long delay spreads. Due to the geometric structure of deep oceans, the channel delay spread between the direct paths and surface/bottom reflections can be on the order of hundreds of milliseconds, which is usually several times larger than the ZP-OFDM block duration, leading to severe interblock-interference (IBI). Channel time variation also introduces inter-carrier-interference (ICI). The proposed receiver addresses both IBI and ICI in a unified factor-graph representation. Data detection is performed according to the Gaussian message passing (GMP) principle which operates on the factor graph in an iterative manner. The proposed structure is further extended to the progressive/iterative framework, while the channel estimation is updated in each iteration loop. Compared with a multiuser receiver, which views overlapped blocks as coming from different users, the proposed receiver enjoys better block-error-rate performance and appealing expansion capability when multiple receiving-elements are used. Both simulation and experimental results are provided to validate the performance of the proposed receiver.Index Terms-OFDM, deep water communications, interblock-interference, inter-carrier-interference, sparse channel estimation, factor graph, progressive, turbo principle.

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Unified design of iterative receivers using factor graphs

Cited by 194 publications

References 33 publications

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

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

Turbo Equalization: An Overview

A factor-graph based ZP-OFDM receiver for deep water acoustic channels

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