Improved equalization for digital recording using nonlinear filtering and error confinement

Nair, S.K.; Moon, Jaekyun

doi:10.1109/20.334041

Cited by 9 publications

(1 citation statement)

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“…There are two fixes for this problem. The first is to employ a nonlinear filter (neural network or Volterra series filter) that is capable of overcoming these nonlinear distortions; see [24] and [25]. Although recently very popular, such a method introduces further correlation between noise samples due to the nonlinear character of the filter.…”

Section: Adaptive Statistics Trackingmentioning

confidence: 99%

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%