Modeling of Writing Process for Two-Dimensional Magnetic Recording and Performance Evaluation of Two-Dimensional Neural Network Equalizer

Yamashita, Masato; Okamoto, Yoshihiro; Nakamura, Yūichi; Osawa, Hisashi; Miura, K.; Greaves, S.J.; Aoi, H.; Kanai, Y.; Muraoka, H.

doi:10.1109/tmag.2012.2194988

Cited by 26 publications

(7 citation statements)

References 15 publications

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“…where y(i, j) is the equivalent decimal number converted from the binary string of the j th -gene. The hyperparameters M F 1 and M F 2 having eight levels (2)(3)(4)(5)(6)(7)(8)(9) are each represented by {000, 001, 011, . .…”

Section: Nfe Optimized By Genetic Algorithm (Ga)mentioning

confidence: 99%

“…The least mean square (LMS) and recursive least square (RLS) linear adaptive filters [7] improve the BER performance of the linear channels; however, they deteriorate when applied to the nonlinear channels. Meanwhile, the nonlinear symbol detectors based on nonlinear classification decisions, such as Volterra equalizer (VE) [8], neural network equalizer (NNE) [9], hybrid VE-NNE [8], fuzzy logic equalizer (FLE) [10], and NFE [11] have gained more attention in various nonlinear channels, including MR channels, due to the significant BER improvement.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Wongsathan

Supnithi

2021

ECTI-EEC

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Nonlinear distortions caused by partial erasure and nonlinear transition shifts interacting with inter-symbol interference, are a major hindrance to data storage systems, since they degrade detector performance. This work aims to design and optimize the neuro-fuzzy equalizer (NFE) using the multi-objective genetic algorithm (MOGA) to detect nonlinear high-density magnetic recording (MR) channels. Through the GA-assisted back-propagation algorithm and least mean square optimization, the complexity in terms of decision rules is reduced by 25% and significantly provides 65% lower signal processing computation. When applied to the perpendicular (MR) system, the proposed NFE outperforms existing equalizers such as the neural network-based equalizer, fuzzy logic equalizer, and conventional NFE for the Volterra and jitter media noise channels using 1–3 dB and 1.5–3.5 dB signal-to-noise ratio gains at the bit-error-rate of 10-4, respectively. Furthermore, compared to the other models, the NFE provides a more effective output mean square error performance for retrieving the original bit data.

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Section: Nfe Optimized By Genetic Algorithm (Ga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Wongsathan

Supnithi

2021

ECTI-EEC

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“…Thus, we assume that the 5 th island (or the target island) is being written, the 1 st -4 th islands are the previously recorded bits (i.e., there are 16 possible data patterns as listed in Table I), and the 6 th -9 th islands are the next written bits. The triangular write pole 27,28 with an along-track width of 93.5 nm and an across-track length of 50.5 nm is utilized to write the FIG. 1.…”

Section: Micromagnetic Modelingmentioning

confidence: 99%

Investigation of writing error in staggered heated-dot magnetic recording systems

Tipcharoen

Warisarn

Tongsomporn³

et al. 2017

AIP Advances

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To achieve an ultra-high storage capacity, heated-dot magnetic recording (HDMR) has been proposed, which heats a bit-patterned medium before recording data. Generally, an error during the HDMR writing process comes from several sources; however, we only investigate the effects of staggered island arrangement, island size fluctuation caused by imperfect fabrication, and main pole position fluctuation. Simulation results demonstrate that a writing error can be minimized by using a staggered array (hexagonal lattice) instead of a square array. Under the effect of main pole position fluctuation, the writing error is higher than the system without main pole position fluctuation. Finally, we found that the error percentage can drop below 10% when the island size is 8.5 nm and the standard deviation of the island size is 1 nm in the absence of main pole jitter.

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“…On the other hand, large deviation in grain centers leads to excessive increase in grain size dispersion. Yamashita et al [8] used the Poisson disk distribution and Lloyd's relaxation method for the position of the grain centers. This model generates more realistic randomness and grain size dispersion.…”

Section: Introductionmentioning

confidence: 99%

“…However, the write process does not include intergranular exchange fields and magnetostatic interaction fields between grains. In this paper, a modified write process is used in order that magnetic clusters are naturally formed by including these fields [8]. The reader output is generated via the 2-D convolution of the magnetization and the reader sensitivity function.…”

Section: Introductionmentioning

confidence: 99%

A Study of TDMR Signal Processing Opportunities Based on Quasi-Micromagnetic Simulations

Vasić¹,

Khatami²,

Nakamura³

et al. 2015

IEEE Trans. Magn.

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This paper presents the results of a comprehensive study of the shingled writing process and various signal processing and data detection approaches applied to the readback waveforms. The recording simulations include realistic head fields, a random granular media, magnetostatic and exchange interactions, and a READ head sensitivity function. Readback waveforms are examined in both one and two dimensions in terms of signal characteristics (linear and nonlinear), noise behavior (stationary and signal-dependent), and intertrack interference. Different equalization and detection approaches are compared and about a 10% density gain is reported for such 2-D magnetic recording compared with traditional single-track recording. These gains depend strongly on the number of readers, the reader positioning, and the reader width.Index Terms-2-D magnetic recording (TDMR), data-dependent noise, intersymbol interference (ISI), multitrack equalization and detection, shingled magnetic recording.

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Modeling of Writing Process for Two-Dimensional Magnetic Recording and Performance Evaluation of Two-Dimensional Neural Network Equalizer

Cited by 26 publications

References 15 publications

Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Optimal Neuro-Fuzzy Equalizers for Detecting Nonlinear Distortion Channels of the Perpendicular Magnetic Recording System

Investigation of writing error in staggered heated-dot magnetic recording systems

A Study of TDMR Signal Processing Opportunities Based on Quasi-Micromagnetic Simulations

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