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Vasic_Generalized_Belief_Propa ...
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Final Accepted Manuscript
Affiliation
Department of Electrical and Computer Engineering, University of ArizonaIssue Date
2016-07Keywords
channel capacitychannel coding
computational geometry
intersymbol interference
magnetic recording
parity check codes
LDPC
TDMR detector
Voronoi based TDMR channel model
decoder
generalized belief propagation
low-density parity check codes
magnetic grains
media noise
two dimensional intersymbol interference
two dimensional magnetic recording
Algorithm design and analysis
Approximation algorithms
Channel models
Erbium
Media
Signal processing algorithms
Two dimensional displays
Metadata
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IEEECitation
C. K. Matcha, M. Bahrami, S. Roy, S. G. Srinivasa and B. Vasić, "Generalized belief propagation based TDMR detector and decoder," 2016 IEEE International Symposium on Information Theory (ISIT), Barcelona, 2016, pp. 210-214. doi: 10.1109/ISIT.2016.7541291Rights
Copyright © 2016, IEEE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Two dimensional magnetic recording (TDMR) achieves high areal densities by reducing the size of a bit comparable to the size of the magnetic grains resulting in two dimensional (2D) inter symbol interference (ISI) and very high media noise. Therefore, it is critical to handle the media noise along with the 2D ISI detection. In this paper, we tune the generalized belief propagation (GBP) algorithm to handle the media noise seen in TDMR. We also provide an intuition into the nature of hard decisions provided by the GBP algorithm. The performance of the GBP algorithm is evaluated over a Voronoi based TDMR channel model where the soft outputs from the GBP algorithm are used by a belief propagation (BP) algorithm to decode low-density parity check (LDPC) codes.Version
Final accepted manuscriptAdditional Links
http://ieeexplore.ieee.org/document/7541291/ae974a485f413a2113503eed53cd6c53
10.1109/ISIT.2016.7541291