Modeling of writing process for two-dimensional magnetic recording and performance evaluation of two-dimensional neural network equalizer

Masato Yamashita, Yoshihiro Okamoto, Yasuaki Nakamura, Hisashi Osawa, Kenji Miura, Simon J. Greaves, Hajime Aoi, Yasushi Kanai, Hiroaki Muraoka

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

Modeling of a simple writing process considering intergranular exchange fields and magnetostatic interaction fields between grains is studied for two-dimensional magnetic recording (TDMR). A new designing method of a two-dimensional neural network equalizer with a mis-equalization suppression function (2D-NNEMS) for TDMR is also proposed. The bit-error rate (BER) performance of a low-density parity-check coding and iterative decoding system with the designed 2D-NNEMS is obtained via computer simulation using a read/write channel model employing the proposed writing process under TDMR specifications of 4 Tb/in$ 2, and it is compared with those for one- and two-dimensional finite impulse response equalizers (FIREs). It is clarified that the BER performance for the designed 2D-NNEMS is far superior to those for the FIREs.

Original languageEnglish
Article number6333011
Pages (from-to)4586-4589
Number of pages4
JournalIEEE Transactions on Magnetics
Volume48
Issue number11
DOIs
Publication statusPublished - 2012

Keywords

  • Channel modeling
  • genetic algorithms
  • magnetic cluster
  • two-dimensional magnetic recording (TDMR)
  • two-dimensional neural network equalizer (2D-NNE)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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