Neural network equalizer for GPRML system with post-processor

Hisashi Osawa, Masayuki Kawae, Yoshihiro Okamoto, Yasuaki Nakamura, Hiroaki Muraoka

Research output: Contribution to journalConference articlepeer-review

Abstract

The neural network equalizer (NNE) for the generalized partial response maximum likelihood (GPRML) system with a postprocessor in perpendicular magnetic recording is studied. First, a new designing method of NNE is proposed for suppressing frequency of a high-level noise at the discrimination point which degrades the bit error rate (BER) performance of the GPRML system with a post-processor. Then, the BER performance of GPR class-1 ML system with a post-processor using the NNE is obtained and compared with that using a conventional transversal filter as an equalizer. The result shows that the gain of the former over the latter is about 0.6 dB at a BER of 10-5.

Original languageEnglish
Pages (from-to)75-82
Number of pages8
JournalPhysics Procedia
Volume16
DOIs
Publication statusPublished - 2011
Event9th Perpendicular Magnetic Recording Conference, PMRC 2010 - Sendai, Japan
Duration: 2011 May 172011 May 19

Keywords

  • GPRML system
  • Hybrid genetic algorithm
  • Neural network equalizer
  • Perpendicular magnetic recording
  • Post-processor

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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