Read Channel Modeling and Neural Network Block Predictor for Two-Dimensional Magnetic Recording

Various schemes have been investigated to study the modeling of recording media, the writing/reading process, and the information recovery in magnetic recording systems, such as two-dimensional magnetic recording (TDMR) and bit-patterned media recording (BPMR). As the bit scales down to the size of...

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Bibliographic Details
Published inIEEE transactions on magnetics Vol. 56; no. 1; pp. 1 - 5
Main Authors Luo, Ke, Wang, Shaobing, Xie, Guoqiang, Chen, Wei, Chen, Jincai, Lu, Ping, Cheng, Weiming
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Artificial neural networks
Computer simulation
Data recovery
Detectors
Harmful patterns
Heuristic methods
Interference
Magnetic recording
Magnetic storage
Magnetic tape
Magnetism
Modelling
neural network
Neural networks
Predictive models
read process
Recording instruments
Reliability
Signal processing
Two dimensional models
two-dimensional magnetic recording (TDMR)
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Summary:Various schemes have been investigated to study the modeling of recording media, the writing/reading process, and the information recovery in magnetic recording systems, such as two-dimensional magnetic recording (TDMR) and bit-patterned media recording (BPMR). As the bit scales down to the size of the media particles, the channel modeling and simulation become more complicated because of the increasing media noise. Meanwhile, it relies on more powerful 2-D signal-processing algorithms error control codes. The contributions of this article are three-fold: 1) we improve the read process in terms of our proposed reliability for read response that captures the effects of inter-track interference (ITI), inter-symbol interference (ISI), and the randomness of grains; 2) we present a neural network block predictor (NNBP) and a logistic regression method to provide heuristic methods in studying data recovery; and 3) we investigate the most possible harmful transitions and patterns. Results show the NNBP achieves good performance and is good at detecting those totally isolated bits that are thought to be bad patterns from an intuitive point of view.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2019.2950704