Multi-level decision feedback equalization: an efficient realization of FDTS/DF

Kenney, J.G.; Wood, Roger

doi:10.1109/20.364794

Cited by 39 publications

(19 citation statements)

References 12 publications

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“…Typically this is done with a least mean squaresclass (LMS) algorithm that ensures that the mean of the signal samples is close to these target values. In decision feedback equalization (DFE) based detectors or hybrids between fixed delay tree search and DFE, such as FDTS/DF [5] or MDFE [6], [7], the target response need not be prespecified. Instead, the target values are chosen on the fly by simultaneously updating the coefficients of the front end and feedback equalizers with an LMS-type algorithm; for details see [23].…”

Section: Adaptive Statistics Trackingmentioning

confidence: 99%

“…In the derivations of the branch metrics (8), (10), and (13), we made no assumptions on the exact Viterbi-type architecture, that is, the metrics can be applied to any Viterbi-type algorithm such as PRML [3], [4], FDTS/DF [5], RAM-RSE [8], or MDFE [6], [7]. In Section IV, we show results on how the chosen metric affects the performance of several class-4 partial response (PR4 and EPR4) detectors.…”

Section: ) Correlation-sensitive Branch Metricmentioning

confidence: 99%

“…The traditional peak detectors (PD) [1] have been replaced by Viterbi-like detectors [2] in the form of partial response maximum likelihood (PRML) schemes [3], [4] or hybrids between tree/trellis detectors and decision feedback equalizers (DFE) like fixed delay tree search with decision feedback (FDTS/DF) [5], multilevel decision feedback equalization (MDFE) [6], [7], and random access memory reduced-state sequence estimation (RAM-RSE) [8]. These methods were derived under the assumption of additive white Gaussian noise (AWGN) in the system.…”

Section: Introductionmentioning

confidence: 99%

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Correlation-sensitive adaptive sequence detection

Kavcic

Moura

1998

IEEE Trans. Magn.

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Abstract-In high density magnetic recording, noise samples corresponding to adjacent signal samples are heavily correlated as a result of front-end equalizers, media noise, and signal nonlinearities combined with nonlinear filters to cancel them. This correlation significantly deteriorates the performance of detectors at high densities. In this paper, we propose a novel sequence detector that is correlation sensitive and adaptive to the nonstationary signal sample statistics. We derive the correlationsensitive maximum likelihood detector. It can be used with any Viterbi-like receiver (e.g., partial response maximum likelihood, fixed delay tree search, multilevel decision feedback equalization) that relies on a tree/trellis structure. Our detector adjusts the metric computation to the noise correlation statistics. Because these statistics are nonstationary, we develop an adaptive algorithm that tracks the data correlation matrices. Simulation results are presented that show the applicability of the new correlationsensitive adaptive sequence detector.

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Section: Adaptive Statistics Trackingmentioning

confidence: 99%

Section: ) Correlation-sensitive Branch Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Correlation-sensitive adaptive sequence detection

Kavcic

Moura

1998

IEEE Trans. Magn.

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“…As a result, adding or removing a constant from all the metrics will not affect the detector in an algorithmic sense. With this in mind, a simplification is apparent if the branch metric (6) is expanded to give (11) The term is common to all the metrics, so it is removed to give a new branch metric defined as (12) Note that in (5) the values of are taken from so there are no more than 2 unique magnitude values for . The other half of the tree is composed of values that differ from the first half only in sign.…”

Section: B Simplified Metric Computationmentioning

confidence: 99%

“…At the other extreme, FDTS/DF is the same as the classic DFE, which has been studied in detail in terms of both theory and architecture [6]- [8]. The particular case of FDTS/DF used with a run-length limited (RLL) codes has also received much attention [9] along with the variation known as multilevel DFE (MDFE) [10]- [12]. This paper illustrates the tradeoffs between performance and tree depth using simulations with several modulation codes.…”

Section: Introductionmentioning

confidence: 99%

Architectures for the implementation of a fixed delay tree search detector

Brickner

Moon²

1997

IEEE Trans. Magn.

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This paper examines the tradeoff between fixed delay tree search (FDTS) detector complexity and performance with various modulation codes. Several architectures suitable for implementing FDTS or achieving performance comparable to FDTS are presented. Recursive forms are derived by decomposing the branch metric computation while breaking down the entire path metric yields nonrecursive forms. The final architecture casts the detection problem into a signal space context in which the observation space is partitioned into decision regions. These structures are presented and evaluated in the context of an analog very large scale integration (VLSI) implementation. Compared to a direct mapping to hardware of the original algorithm, these alternative schemes offer reduced power consumption and/or increased data rate.Index Terms-Decision feedback equalizers, digital magnetic recording, fixed delay tree search, multidimensional signal detection, sampled data systems, signal detections.

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