Mitigating the Effects of Track Mis-Registration in Single-Reader/Two-Track Reading BPMR Systems

Warisarn, Chanon

doi:10.1109/tmag.2019.2891059

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Figure 10 shows the BER performance of our proposed TMR correction, which consists of a CNN-histogram-based TMR estimator and CNN-based data detector with group detection scheme denoted by "Prop. system I" compared with the "System I," which is the conventional SRTR BPMR system without TMR correction where used 1-D equalizer and 1-D monic constrained GPR target designed for a non-TMR situation in the data detection process, and "System II" represents the system that uses a pair of 1-D equalizer and 1-D monic constrained GPR target designed according to each TMR level in the data detection process and detect data bits by PRML-based detector [8]. In this work, the coefficients of GPR target and equalizer in "System I" and "System II" are set to be 1×3 and 1×11 taps, respectively [8].…”

Section: B Ber Performancesmentioning

confidence: 99%

“…For example, S. Nabavi proposed the modified Viterbi algorithm to mitigate the ITI effect and deliver better BER performance compared to an original Viterbi algorithm under the system that experiences the unknown TMR [7]. C. Warisarn proposed TMR estimation by using the energy ratio of the readback signal on a single-reader/two-track reading (SRTR) BPMR, which corrects the effect of TMR using a proper one-dimensional (1-D) generalized partial response (GPR) target and 1-D equalizer that was predesigned corresponding to the estimated TMR levels [8].…”

Section: Introductionmentioning

confidence: 99%

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Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems

Kankhunthod

Warisarn

2023

IEEE Access

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One of the problems that cause a decrease in the performance of the ultra-high bit-patterned magnetic recording (BPMR) system is track misregistration (TMR). Since the gap between data tracks is extremely narrow, it easily affects keeping the reader in the desired position. Therefore, this paper proposes the track misregistration mitigation included the estimation and correction techniques on singlereader/two-track reading (SRTR) BPMR using only a readback signal. The TMR estimation technique uses the convolutional neural network (CNN) to estimate the TMR level by the histograms of the readback signal enabling minimization of the complexity of the CNN structure and amount of training time. The estimated TMR levels obtained from the proposed CNN-histogram-based method will then be utilized to detect the estimated recorded bit by the CNN-based data detector. The simulation shows that our proposed system provides better TMR prediction accuracy even though the system has to face higher media noise. Furthermore, the CNN-based data detectors perform superior to the partial response maximum likelihood (PRML) based data detector, especially in strong electronic noise situations and the severe imperfection of recording media.INDEX TERMS Bit-patterned magnetic recording (BPMR), convolutional neural network (CNN), supervised learning, track misregistration (TMR).

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Section: B Ber Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems

Kankhunthod

Warisarn

2023

IEEE Access

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“…However, the simulation indicates that the proposed detection algorithm can still provide obvious SNR gains compared to the conventional algorithm when the TMR (i.e., TMR/TW HIMR ∈ [-11.5%, 11.5%]) exists, because using data set with various TMR effects to train the neural network and variable equalizer guarantees the generalization ability of proposed algorithm. Additionally, the TMR can be predicted during the detection process, since researchers have studied various prediction methods such as utilizing the relationship between the energy ratio of readback signal and head offset [23] or designing the 2D asymmetric target best fit to the estimated TMR level [24]. Such TMR prediction methods can be utilized to correct the read head position before implementing the proposed multitrack detection algorithm.…”

Section: ) Equalizer and Detector Designmentioning

confidence: 99%

Detection Scheme With Joint Intertrack Interference and Media Noise Mitigations for Heat Assisted Interlaced Magnetic Recording

Chen

Wang

et al. 2022

IEEE Access

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Heat assisted interlaced magnetic recording (HIMR) is a promising candidate for the nextgeneration of magnetic recording technology to further increase the area density beyond 1Tb/in 2 . Specifically, the high temperature and low temperature tracks are written in an interlaced order to improve the recording performance. However, the inter-track interference (ITI), inter-symbol interference (ISI) and thermal jitters brought by the increased recording density and Curie temperature variations are severe in HIMR, which degrade the bit error rate (BER) performance obviously. In this study, we propose a multitrack detection scheme with joint intertrack interference and media noise mitigations. Here a multi-task neural network (MTNN) is designed to simultaneously predict ITI pattern and residual media noise, then the 2D variable equalizers corresponding to different ITI patterns are implemented and predicted residual media noises are embedded into the branch metrics of modified Bahl-Cocke-Jelinek-Raviv (BCJR) detector to mitigate ITI and whiten media noise. The simulation demonstrates that the proposed MTNN with variable equalizer and modified BCJR detector (MTNN+VE+MB) algorithm mitigates the ITI and media noise effectively. At the channel bit density of 3.10 Tb/in 2 , it provides 2.6 dB signal-to-noise ratio (SNR) gain compared to that of conventional 2D fixed equalizer with pattern dependent noise prediction detector (FE+PDNP) for the low temperature (LT) tracks with 4% Curie temperature variance.INDEX TERMS Intertrack interference (ITI), Media noise, Heat assisted interlaced magnetic recording (HIMR), Neural network, Multitrack detection.

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“…The bit prediction from the readback signal utilizes a potentially versatile BPMR with a decoding accuracy of ~97% for rate-4/5 modulation codes. It continues with the same network used for single-reader two-track reading (SRTR) systems [21]- [22] with a validity of ~75% from an SNR of 1 to 8 dB. Ultimately, our study offers twofold contributions: a new decoder method that freely adapts to any BPMR channel designs from a hard drive processing perspective, and a sequential data scheme for future magnetic recording channel designs in unsupervised learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

2020

Self Cite

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Dynamic bit encoding and decoding of the magnetic recording process remain a challenge in that the process is restrained by the balance between reading and writing performance of the decoder's bit error rate (BER). Sequential neural networks offer data streamflow for processes to reproduce recoded bits from signal distribution, overcoming the limitation of codeword mapping designed for each specific bitpatterned magnetic recording (BPMR) channel. Here, we implement the vanilla long short-term memory (LSTM) for adaptive modulation decoders in various BPMR channel designs within a single network, which benefits multi-channel decoder calibration tools with the same standardization. Signal information from media readback, a two-dimensional (2D) equalizer, 2D Viterbi, and a 2D soft-output Viterbi algorithm (SOVA) detector is arranged as a tensor that enables sequence-to-sequence bit prediction even with a highly complex data arrangement. Our adaptive model can predict recorded bits from readback with accuracies of approximately 97% for rate 4/5 decoding and 75% for crossing platforms, using a recently proposed singlereader/two-track reading (SRTR) system at an areal density of 4 Tb/in 2 in a signal-to-noise ratio range of 1 to 8 dB. We conducted a BER simulation with the relevant results from conventional decoders and the LSTM model. Ultimately, our approach may demonstrate the limitation of supervised learning designed for BPMR systems and reveal a sequence data focus on LSTM that paves the way for sequential-type, unsupervised, mechanism-based, next-generation magnetic recordings.INDEX TERMS Long-short term memory (LSTM), supervised learning, deep learning, bit-patterned media recording (BPMR), channel decoding

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Mitigating the Effects of Track Mis-Registration in Single-Reader/Two-Track Reading BPMR Systems

Cited by 8 publications

References 20 publications

Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems

Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems

Detection Scheme With Joint Intertrack Interference and Media Noise Mitigations for Heat Assisted Interlaced Magnetic Recording

Long-Short Term Memory-Based Application on Adaptive Cross-Platform Decoder for Bit Patterned Magnetic Recording

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