Symmetric information rate estimation and bit aspect ratio optimization for TDMR using Generalized Belief Propagation

Mehrdad Khatami, Mohsen Bahrami, Bane Vasic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In this paper, we propose a method for estimating the Symmetric Information Rate (SIR) for Two Dimensional Magnetic Recording (TDMR) channel by using the Generalized Belief Propagation (GBP) algorithm. We consider the Voronoi model as the channel model of a TDMR system. The dominant component of noise in TDMR caused by the imperfections of the medium is called 'media noise'. The nature of the media noise is data-dependent, however, the media noise can be closely approximated by additive white Gaussian noise (AWGN) with variance and mean dependent on channel bits written on the magnetic medium. Lower and upper bounds on the SIR are obtained by using the GBP algorithm. In addition, it is shown that the accuracy of the SIR estimation can be adjusted for sufficient size of the magnetic medium. Finally, the bit aspect ratio of a TDMR system is optimized by maximizing the SIR per unit area.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1620-1624
Number of pages5
ISBN (Electronic)9781467377041
DOIs
StatePublished - Sep 28 2015
EventIEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, Hong Kong
Duration: Jun 14 2015Jun 19 2015

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2015-June
ISSN (Print)2157-8095

Other

OtherIEEE International Symposium on Information Theory, ISIT 2015
CountryHong Kong
CityHong Kong
Period6/14/156/19/15

Keywords

  • Generalized belief propagation (GBP)
  • Media noise
  • Symmetric information rate (SIR)
  • Two-dimensional magnetic recording (TDMR)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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