Decision feedback equalizers with constrained feedback taps for reduced error propagation

Zoltowski, M.D.; Hillery, W.J.; Fimoff, M.

doi:10.1117/12.496918

Cited by 2 publications

(10 citation statements)

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“…The derivation of these equations is a nontrivial matter and we do not give the derivation here nor explain the notation in detail, in the interests of space, other than to say that H R is a convolution matrix of the channel vector, h, and δ K+L ha +1 is a vector with a 1 in the (K + L ha + 1) th position and zeros elsewhere. For a complete explanation of the notation and a derivation, see [17] and [26]. The filters given are biased and need to be unbiased by multiplication by a scaling factor, [17].…”

Section: B Stochastic Estimatesmentioning

confidence: 99%

“…For a complete explanation of the notation and a derivation, see [17] and [26]. The filters given are biased and need to be unbiased by multiplication by a scaling factor, [17].…”

Section: B Stochastic Estimatesmentioning

confidence: 99%

“…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%

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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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Section: B Stochastic Estimatesmentioning

confidence: 99%

“…For a complete explanation of the notation and a derivation, see [17] and [26]. The filters given are biased and need to be unbiased by multiplication by a scaling factor, [17].…”

Section: B Stochastic Estimatesmentioning

confidence: 99%

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%

See 2 more Smart Citations

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

Pladdy

2006

Milcom 2006

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“…While the DFE ideally performs better than a linear equalizer [1], in reality its performance is reduced by error propagation. The performance penalty can be significant in cases of severe ISI [2] so that it becomes necessary to take steps to reduce the effect of error propagation. Various methods for mitigating error propagation have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Computation of a constrained decision feedback equalizer for reduced error propagation

Hillery

Zoltowski

Fimoff³

2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733)

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It has been previously shown that the effects of error propagation in a decision feedback equalizer may be reduced by constraining the 2-norm of the feedback filter. This paper describes three algorithms for calculating the constrained equalizer based on a channel estimate. An iteration function is derived for the Karush-Kuhn-Tucker multiplier and the algorithms are designed to quickly find a fixed point of this function. The algorithms include a modified regula falsi algorithm known as the Pegasus iteration, a modified secant algorithm, and a nonlinear fit to the iteration function. At each iteration, the conjugate gradient algorithm is used to solve the resulting linear equations. The algorithms are demonstrated on the 8-VSB terrestrial broadcast digital TV system. While all three algorithms exhibit good convergence characteristics, the nonlinear fit converges the fastest.

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Decision feedback equalizers with constrained feedback taps for reduced error propagation

Cited by 2 publications

References 0 publications

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

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

Computation of a constrained decision feedback equalizer for reduced error propagation

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