The minimum description length principle for modeling recording channels

Kavcic, A.; Srinivasan, M.

doi:10.1109/49.920180

Cited by 10 publications

(5 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both parameters should be chosen small enough to avoid unnecessary complexity. A systematic approach to parameter estimation is proposed for a more general parameter set in [8]. These generalized parameters allow the use of noncausal noise filters.…”

Section: B Model Generationmentioning

confidence: 99%

Performance of a signal-dependent autoregressive channel model

Stander

Patapoutian²

2000

IEEE Trans. Magn.

View full text Add to dashboard Cite

Recently, work has been published on a channel model that employs signal-dependent autoregressive filters to accurately and efficiently model signal nonlinearities and media noise characteristics, such as signal-dependence and correlation, which are common in magnetic recording signals. This paper evaluates the model's performance using 42 sets of spin-stand data that cover a large range of head/media manufacturers and operating points.

show abstract

Section: B Model Generationmentioning

confidence: 99%

Performance of a signal-dependent autoregressive channel model

Stander

Patapoutian²

2000

IEEE Trans. Magn.

View full text Add to dashboard Cite

show abstract

“…8. The discrete-time output of the model is given by (14) where is the channel input sequence, is the equalized transition response, is a random noise sequence, is the media noise sequence reflecting the amount of position jitter, and is the additive noise sequence. The channel input sequence is determined by the transition sequence and a random part due to the transition jitter.…”

Section: A Media Noise Modelmentioning

confidence: 99%

“…Hence, the knowledge of the parameter values provides important information for the development and evaluation of recording media as well as detectors for next-generation disk drives. All these parametric models can be considered to be special cases of the signal-dependent autoregressive (AR) channel model [14], [15]. In this model, the media noise is the output of an AR system driven by white noise and whose parameters are signal-dependent.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Decomposition of Noise Sources in Digital Recording Systems With Media Noise

2007

View full text Add to dashboard Cite

In digital recording systems, the total amount of data-dependent media noise increases considerably as recording densities increase. A proper noise characterization is crucial for the design of receivers for high-density storage systems. This characterization involves the selection of a proper noise model and subsequently the accurate estimation of the parameters of the selected model. The estimation algorithm proposed in this paper jointly estimates the parameters of both media and additive noise with a high accuracy. The proposed algorithm makes use of the data dependency of the media noise to distinguish between the different noise sources. The algorithm is simple and as a result can be implemented in recording systems, with only a limited amount of complexity, as an easy "add-on" to read-channel ICs. From the simulation results and the analytical derivation of the estimation algorithm, we can clearly indicate which data patterns yield near-optimal estimation performance. These patterns are the ideal test patterns in experimental systems. We propose and discuss test patterns for magnetic and optical storage systems.

show abstract

“…The idea is to rederive the predictor conditioned on the segment rather than , where and . It can be shown that the predictor that minimizes the error variance conditioned on is given by (14) and the resulting conditional error variance by (15) where , , and are obtained by averaging ,…”

Section: B Reducing the Number Of Required Predictorsmentioning

confidence: 99%

“…In that sense, the method used here can also provide a guideline as to finding the right initial model parameters for the approach of [5]. Note that there also has been a recent development on more rigorous approaches to identify the model parameters [14].…”

Section: A Estimation Of Parameters Andmentioning

confidence: 99%

Pattern-dependent noise prediction in signal-dependent noise

Moon

Park

2001

IEEE J. Select. Areas Commun.

112

View full text Add to dashboard Cite

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 position-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.Index Terms-Markov process, maximum likelihood sequence detection, noise prediction, reduced-complexity sequence detector, signal-dependent noise.

show abstract

The minimum description length principle for modeling recording channels

Cited by 10 publications

References 41 publications

Performance of a signal-dependent autoregressive channel model

Performance of a signal-dependent autoregressive channel model

Adaptive Decomposition of Noise Sources in Digital Recording Systems With Media Noise

Pattern-dependent noise prediction in signal-dependent noise

Contact Info

Product

Resources

About