Back to Search
Start Over
Read Channel Modeling and Neural Network Block Predictor for Two-Dimensional Magnetic Recording
- Source :
- IEEE Transactions on Magnetics. 56:1-5
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- 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.
- Subjects :
- 010302 applied physics
Artificial neural network
business.industry
Computer science
01 natural sciences
Electronic, Optical and Magnetic Materials
Data recovery
Noise
Interference (communication)
0103 physical sciences
Electrical and Electronic Engineering
Error detection and correction
business
Algorithm
Block (data storage)
Communication channel
Subjects
Details
- ISSN :
- 19410069 and 00189464
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Magnetics
- Accession number :
- edsair.doi...........e2a8227de2af148210a9bf97b0dd693e