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Pattern-dependent noise prediction in signal-dependent noise

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2 Author(s)
Jaekyun Moon ; Commun. & Data Storage Lab., Minnesota Univ., Minneapolis, MN, USA ; Jongseung Park

Maximum and near-maximum likelihood sequence detectors in signal-dependent noise are discussed. It is shown that the linear prediction viewpoint allows a very simple derivation of the branch metric expression that has previously been shown as optimum for signal-dependent Markov noise. The resulting detector architecture is viewed as a noise predictive maximum likelihood detector that operates on an expanded trellis and relies on computation of branch-specific, pattern-dependent noise predictor taps and predictor error variances. Comparison is made on the performance of various low-complexity structures using the positional-jitter/width-variation model for transition noise. It is shown that when medium noise dominates, a reasonably low complexity detector that incorporates pattern-dependent noise prediction achieves a significant signal-to-noise ratio gain relative to the extended class 4 partial response maximum likelihood detector. Soft-output detectors as well as the use of soft decision feedback are discussed in the context of signal-dependent noise

Published in:

Selected Areas in Communications, IEEE Journal on  (Volume:19 ,  Issue: 4 )

Date of Publication:

Apr 2001

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