Mitigating error propagation effects in a decision feedback equalizer

Reuter, M.; Allen, J C; Zeidler, J.R.; North, R.C.

doi:10.1109/26.966079

Cited by 62 publications

(33 citation statements)

References 25 publications

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“…This approach was applied to the DFE structures with an additional constant on the norm of the feedback filter taps [3]. In our approach, we apply multiple weight factors for each error statistics,…”

Section: A Bidirectionalitymentioning

confidence: 99%

Noncausal and Bidirectional Soft Decision Feedback Equalizer for Turbo Equalization

Lee

Gelfand²

2008

2008 4th IEEE International Conference on Circuits and Systems for Communications

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The goal of this paper is to develop equalizers which can provide soft information to channel decoders for use in the initial stage of iteration, of equalization and decoding architectures. We propose an initial-stage equalization structure by combining noncausality, bidirectionality and soft processing for common non-initial type equalizers such as ML, MAP, or soft interference, which typically requires sufficiently accurate previous information to perform some type of soft cancellation, especially for hostile channel conditions which are hard to equalize.

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Section: A Bidirectionalitymentioning

confidence: 99%

Noncausal and Bidirectional Soft Decision Feedback Equalizer for Turbo Equalization

Lee

Gelfand²

2008

2008 4th IEEE International Conference on Circuits and Systems for Communications

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“…Several authors have considered constrained MMSE DFE tap-weight calculation, where the constraint is designed to impede error propagation, [17], [18], [19], [20], [21]. The constraints which we derive may be used in this context.…”

Section: Introductionmentioning

confidence: 98%

“…This further development will be in a follow-up paper together with our own simulations. Simulations of constrained DFEs are given in each of [17], [18], [19], [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Constrained DFEs for Reduced Error Propagation: Theoretical Results and an Adaptive Soft Error Variance Controlled DFE

Pladdy

2006

Milcom 2006

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The problem of error propagation in a decision feedback equalizer (DFE) is acknowledged to be an important factor in the functioning of the equalizer. In order to determine conditions which guarantee the non-propagation of errors we formulate the DFE as a dynamical system in the form of a matrix equation. We analyse the propagation of errors in this matrix system and formulate conditions on the system matrices which guarantee: a) Using deterministic analysis, the non-propagation of errors; b) Using a stochastic analysis, the non-propagation of the expected value of the norm of the hard error vector. These conditions translate into constraints on the size of the feedback tap-weights so that there is linear convergence of the errors (or of expected values of norms of errors for the stochastic analysis) with constant C < 1. This is equivalent to exponential decay of the hard errors (or of expected values of norms of errors for the stochastic analysis) over time with decay constant C < 1. This ensures that errors do not propagate, but decay exponentially with time.The stochastic analysis gives weaker constraints which may be of more practical use in filter design, as the deterministic constraint guards against a worst case scenario for error propagation. The constraints derived in the stochastic case also combine the variance of the soft errors. Hence feedback of soft error variance may be used to adapt the constraint used on the feedback filter, and this motivates an algorithm for a soft error variance controlled adaptive DFE.

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“…We also do not focus on the effect of errors in other components of the system (specifically, the feedback filter. Error propagation in the feedback filter has been an emphasis of prior research-see, for instance, [14], [44] and [46]). …”

Section: The Problem Statementmentioning

confidence: 99%

Direct-form adaptive equalization for underwater acoustic communication

Yellepeddi¹

2012

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Adaptive equalization is an important aspect of communication systems in various environments. It is particularly important in underwater acoustic communication systems, as the channel has a long delay spread and is subject to the effects of timevarying multipath fading and Doppler spreading.The design of the adaptation algorithm has a profound influence on the performance of the system. In this thesis, we explore this aspect of the system. The emphasis of the work presented is on applying concepts from inference and decision theory and information theory to provide an approach to deriving and analyzing adaptation algorithms. Limited work has been done so far on rigorously devising adaptation algorithms to suit a particular situation, and the aim of this thesis is to concretize such efforts and possibly to provide a mathematical basis for expanding it to other applications.We derive an algorithm for the adaptation of the coefficients of an equalizer when the receiver has limited or no information about the transmitted symbols, which we term the Soft-Decision Directed Recursive Least Squares algorithm. We will demonstrate connections between the Expectation-Maximization (EM) algorithm and the Recursive Least Squares algorithm, and show how to derive a computationally efficient, purely recursive algorithm from the optimal EM algorithm.Then, we use our understanding of Markov processes to analyze the performance of the RLS algorithm in hard-decision directed mode, as well as of the Soft-Decision Directed RLS algorithm. We demonstrate scenarios in which the adaptation procedures fail catastrophically, and discuss why this happens. The lessons from the analysis guide us on the choice of models for the adaptation procedure. We then demonstrate how to use the algorithm derived in a practical system for underwater communication using turbo equalization. As the algorithm naturally incorporates soft information into the adaptation process, it becomes easy to fit it into a turbo equalization framework. We thus provide an instance of how to use the information 3 of a turbo equalizer in an adaptation procedure, which has not been very well explored in the past. Experimental data is used to prove the value of the algorithm in a practical context.

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Mitigating error propagation effects in a decision feedback equalizer

Cited by 62 publications

References 25 publications

Noncausal and Bidirectional Soft Decision Feedback Equalizer for Turbo Equalization

Noncausal and Bidirectional Soft Decision Feedback Equalizer for Turbo Equalization

Constrained DFEs for Reduced Error Propagation: Theoretical Results and an Adaptive Soft Error Variance Controlled DFE

Direct-form adaptive equalization for underwater acoustic communication

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