2006 Fiscal Year Final Research Report Summary
Research on Ultrahigh-Density Perpendicular Magnetic Recording Using Adaptive PRML Systems
| Project/Area Number |
17360182
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Ehime University |
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Principal Investigator |
OSAWA Hisashi Ehime University, Graduate School of Science and Engineering, Professor, 理工学研究科, 教授 (50029336)
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| Co-Investigator(Kenkyū-buntansha) |
OKAMOTO Yoshihiro Ehime University, Graduate School of Science and Engineering, Associate Professor, 理工学研究科, 助教授 (20224082)
NAKAMURA Yasuaki Ehime University, Graduate School of Science and Engineering, Research Associate, 理工学研究科, 助手 (50380259)
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| Project Period (FY) |
2005 – 2006
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| Keywords | perpendicular magnetic recording / thermal decay / PRML system / GPRML system / PRML-AR system / neural network equalization / adaptive PRML system |
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| Research Abstract |
A thermal decay model based on the experimental data for three CoPtCr-SiO_2 perpendicular magnetic recording (PMR) media with thermal stability factors K_uV/kT of 94, 60 and 45 is obtained. The long-term BER performance of PRML, GPRML and GPRML-AR systems for the 128/130(0,16/8) RLL code is evaluated by computer simulation using the thermal decay model at a linear recording density of 1000 kBPI, which is difficult to evaluate experimentally.
It is clarified that, in all PRML systems at t_E = 10^8 seconds, there is no degradation in BER performance for the medium of K_uV/kT =94, while an order of magnitude or so degradation in BERs is found for the medium of K_uV/kT =60 and three orders of magnitude or so degradation in BERs can be seen for the medium of K_uV/kT =45. Nevertheless, GPR1ML system provides the SNR improvements of 0.4, 0.4 and 4.9 dB at BER=10^<-4> over PR1ML system for the media of K_uV/k_T =94, 60 and 45, respectively, and GPR1ML-AR system offers the improvements of 1.5, 1.8 and 6.6 dB at t_E=10^6 is seconds.
When a neural network is employed as a equalizer and the network is simplified and optimized by using a hybrid genetic algorithm which is the combination of a back propagation algorithm and a genetic algorithm, the performance degradation of PRML system is not found for K_uV/kT =60 and till t_E = 10^5. Therefore, this shows that the combination of neural network equalization and PRML system has the good adaptability for the thermal decay. Hereafter, the adaptive GPRML and PRML-AR systems using a neural network equalizer will be studied.
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