Performance comparison of detection methods in magnetic recording

Moon, Jaekyun; Carley, L.R.

doi:10.1109/20.102903

Cited by 175 publications

(63 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Define as the length of the leading (anticausal) ISI and as the length of the trailing (causal) ISI, such that . With this notation, we can write the conditional pdf in (3) as (4) Substituting (4) into (2) and applying the Bayes rule, we obtain the factored form of the likelihood function (conditional pdf) (5) If the indexes in (5) exceed , i.e., if , then assume that . This ensures proper treatment of the boundary conditions.…”

Section: ) Finite Correlation Lengthmentioning

confidence: 99%

“…We could combine the causal and anticausal factorization and find the geometric mean of the two to form a causal-anticausal factorization. Since this only complicates derivations and does not provide further insight, we will work only with the anticausal (5).…”

Section: ) Finite Correlation Lengthmentioning

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“…The traditional peak detectors (PD) [1] have been replaced by Viterbi-like detectors [2] in the form of partial response maximum likelihood (PRML) schemes [3], [4] or hybrids between tree/trellis detectors and decision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%

See 2 more Smart Citations

Correlation-sensitive adaptive sequence detection

Kavcic

Moura

1998

IEEE Trans. Magn.

View full text Add to dashboard Cite

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.

show abstract

Section: ) Finite Correlation Lengthmentioning

confidence: 99%

Section: ) Finite Correlation Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Correlation-sensitive adaptive sequence detection

Kavcic

Moura

1998

IEEE Trans. Magn.

View full text Add to dashboard Cite

show abstract

“…However, a number of schemes have been developed that can speed up the process. Perhaps the best known are the fixed-delay tree search [34] and decision feedback modifications [35][36].…”

Section: Bayesian Detectionmentioning

confidence: 99%

Signal Processing for Present and Future Magnetic Recording Systems

et al. 1997

View full text Add to dashboard Cite

Abstract-A background of activity is presented for signal processing as applied to, and used in, magnetic information storage systems. Specific examples are given of how current and previous research projects fit into the overall progress of research in this area. Arguments are presented as to why, in the distant future, information will be stored as "analogue" information rather than binary so methods and techniques will need to be developed to utilize the near-infinite capacity of such systems.

show abstract

“…Advanced signal processing and detection techniques have played an important role in the design of all data storage systems [24], [13], [4], [14], [15], [1], [10]. Indeed techniques such as partial-response max-likelihood [4], [21], [24] were responsible for significantly improving magnetic disk technology.…”

Section: Introductionmentioning

confidence: 99%

Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

Kumar¹,

Agarwal²,

Ramamoorthy³

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

There is an increasing need for high density data storage devices driven by the increased demand of consumer electronics. In this work, we consider a data storage system that operates by encoding information as topographic profiles on a polymer medium. A cantilever probe with a sharp tip (few nm radius) is used to create and sense the presence of topographic profiles, resulting in a density of few Tb per in.2 . The prevalent mode of using the cantilever probe is the static mode that is harsh on the probe and the media. In this article, the high quality factor dynamic mode operation, that is less harsh on the media and the probe, is analyzed. The read operation is modeled as a communication channel which incorporates system memory due to inter-symbol interference and the cantilever state.We demonstrate an appropriate level of abstraction of this complex nanoscale system that obviates the need for an involved physical model. Next, a solution to the maximum likelihood sequence detection problem based on the Viterbi algorithm is devised. Experimental and simulation results demonstrate that the performance of this detector is several orders of magnitude better than the performance of other existing schemes.

show abstract

Performance comparison of detection methods in magnetic recording

Cited by 175 publications

References 35 publications

Correlation-sensitive adaptive sequence detection

Correlation-sensitive adaptive sequence detection

Signal Processing for Present and Future Magnetic Recording Systems

Maximum-Likelihood Sequence Detector for Dynamic Mode High Density Probe Storage

Contact Info

Product

Resources

About