Bit-Flipping Scheme Using K-Means Algorithm for Bit-Patterned Media Recording

As the need for storing significant amounts of data has increased, an increased areal density (AD) of hard disk drives is required. Bit-patterned media recording (BPMR) with the ability to increase AD is regarded as one of the future magnetic storage systems. Reducing the distance between islands st...

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Bibliographic Details
Published in:IEEE transactions on magnetics Vol. 58; no. 8; pp. 1 - 4
Main Authors: Jeong, Seongkwon, Lee, Jaejin
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -means algorithm
Algorithms
Bit-flipping (BF)
bit-patterned media recording (BPMR)
Clustering
Clustering algorithms
Data models
Detectors
Disk drives
Equalizers
Iterative methods
Magnetic storage
Magnetism
Media
Prediction algorithms
Recording
signal detection
Signal processing algorithms
Storage systems
ISSN:0018-9464, 1941-0069
Online Access:Get full text
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Summary:As the need for storing significant amounts of data has increased, an increased areal density (AD) of hard disk drives is required. Bit-patterned media recording (BPMR) with the ability to increase AD is regarded as one of the future magnetic storage systems. Reducing the distance between islands storing 1 bit can provide high AD, but it creates problems of intersymbol and intertrack interferences. In this study, we propose a bit-flipping scheme using the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means algorithm for the BPMR system. In the proposed scheme, the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means algorithm, which is an iterative clustering method for identifying similar samples and assigning them to clusters, is used to assign the samples that consist of the main and neighboring data to clusters. The predicted index from <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means is utilized to compare and flip the sign of the main data when the sign is different. The proposed scheme flips the sign of data that is predicted to be an error and improves the performance using the neighboring information.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2022.3144394