Warisarn, Chanon

Preferred name

Warisarn, Chanon

Alternative Name

Warisarn, C.

Main Affiliation

Integrated Innovative Technology

chanon.wa@kmitl.ac.th

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Symbol-flipping method for block decoding in bit-patterned magnetic recording
(2021-05-19)
Sokjabok, Siwakon
;
Mattayakan, Mutita
;
Warisarn, Chanon
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
Bit-patterned magnetic recording (BPMR) technology can provide an areal density (AD) up to 15 Terabit per square inch (Tb/in2). However, the consequence of an increased AD results in severe inter-symbol interference (ISI) and inter-track interference (ITI). In practice, a run-length limited (RLL) code can be used to alleviate this problem. Therefore, this research proposes a symbol-flipping method in an iterative detection scheme between a soft-output Viterbi algorithm (SOVA) detector and an RLL decoder to help reduce errors resulting from these two interferences in a BPMR system. Simulation results reveal that the proposed system performs better than the same system architecture without the symbol-flipping method by 0.5 decibels at an AD of 5 Tb/in2.
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Study of CNN-Based Data Detection in Dual-Layer Bit-Patterned Magnetic Recording Systems
(2025-01-01)
Sangthong, Siraphop
;
Sokjabok, Siwakon
;
Khametong, Anawin
;
Warisarn, Chanon
This paper introduces an innovative data detection system that utilizes convolutional neural networks (CNNs) for dual-layered bit-patterned magnetic systems. Using a mutual-information CNN architecture, the proposed system tackles the challenge of decoding overlapping readback signals from upper and lower layers. The sliding window detection schemes are implemented with input lengths of 6 (2×3) and 14 (2×7) bits, processing oversampled readback signals from a dataset of 1,000,000 bits. Simulation results conducted over a signal-to-noise ratio range of 10 to 24 dBs indicate that the CNN model with a larger input window significantly outperforms smaller input models and conventional partial response maximum likelihood detectors in terms of bit error rate. These findings illustrate the effectiveness of CNN-based detection in enhancing classification accuracy under high-noise conditions, paving the way for future ultra-high-density magnetic recording systems.
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A Novel ITI Suppression Technique for Coded Dual-Track Dual-Head Bit-Patterned Magnetic Recording Systems
(2020-01-01)
Koonkarnkhai, Santi
;
Warisarn, Chanon
;
Kovintavewat, Piya
Generally, an intertrack interference (ITI) is a critical problem in bit-patterned magnetic recording (BPMR) systems that can attain an areal density (AD) up to 4 Tb/in2. Unavoidably, at high ADs, a very narrow track width must be employed, leading to severe ITI and unacceptable system performance. To tackle the ITI; therefore, this article introduces a novel ITI suppression technique for coded dual-track dual-head (DTDH) BPMR systems. At the first turbo iteration, the weighted readback signal of the adjacent track served as an estimated ITI signal is utilized for subtracting from the target readback signal to subside the ITI effect, before passing the refined readback signal to a turbo equalizer. Nonetheless, for the second turbo iteration onwards, the estimated ITI signal generated by the soft information obtained from a decoder at each turbo iteration will be then employed to subtract from the target readback signal during the turbo decoding process. Computer simulation results demonstrate that the proposed system can provide better performance than the DTDH system using a hard ITI suppression technique as well as the conventional system using one read head to decode one data track for all ADs, because the proposed technique can estimate the ITI signal well. Furthermore, when considering the recording system under the effects of media noise and track mis-registration, we also found that the proposed system is more robust than other systems.
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BER performance improvement using soft-information flipping method in BPMR systems
(2019-07-01)
Busyatras, Wiparat
;
Warisarn, Chanon
Inter-track interference (ITI) cancelation is one of the considerable challenges for high areal density (AD) magnetic recording such as bit-patterned magnetic recording (BPMR) technology. In literature, the two-dimensional (2D) modulation codes have been proposed to cancel the ITI effect which can efficiently improve the overall system performance, e.g., a rate5/6 2D modulation code. Although the rate-5/6 modulation code ensures that the readback signal of the center track will not be corrupted by severe ITI; however, both the upper and lower tracks can still be interfered by their sidetracks, which may lead to some errors in decoding process. To improve this shortcoming, we propose a bit-flipping technique that performs together with the rate-5/6 2D modulation code. Here, the relationship between the data encoding constraint and the soft-information obtained from the soft output Viterbi algorithm (SOVA) detector are utilized to be a criterion for flipping the ambiguous data bits. Simulation results indicate that the proposed system is better than the conventional coded system with and without media noise and track mis-registration (TMR) effects.
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Deep Neural Networks based Soft-Information Improvement for Two-head/Two-Track Bit-Patterned Magnetic Recording
(2022-01-01)
Khametong, Anawin
;
Rueangnetr, Natthakan
;
Warisarn, Chanon
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
To increase an areal density (AD) of an ultra-high density bit-patterned magnetic recording (BPMR) system, we have previously proposed a track misregistration (TMR) correction method combined with the soft information adjustor (SIA) to cope with the effects of TMR and two-dimensional (2D) interference. However, we found that soft information or log-likelihood ratio (LLR) can be improved to earn better bit-error-rate (BER) performances. In this work; therefore, we propose to use two types of deep neural networks (DNNs), i.e., multi-layer perceptron (MLP) and long short-Term memory (LSTM) network with identical parameter magnitude to improve overall system performance. Here, both DNNs are operated with an earlier SIA on a two-head/two-Track (2H2T) BPMR system. Numerical results show that our proposed methods can deliver better BER performance over the earlier SIA system at all TMR levels with and without position jitter noises at the AD of 3.0 Terabit per square inch.
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An MLP-Based ITI Suppression Method for Multi-Head Multi-Track Bit-Patterned Magnetic Recording
(2025-01-01)
Koonkarnkhai, Santi
;
Kovintavewat, Piya
;
Warisarn, Chanon
Inter-track interference (ITI) is a critical challenge in bit-patterned magnetic recording (BPMR) systems, particularly at high areal densities (ADs) where reduced bit period and track pitch lead to severe interference. This article introduces a novel multi-layer perceptron (MLP)-based ITI suppression method for the three-head three-track (3H3T) BPMR system. Our approach uses an MLP to estimate the main track data and uses iterative decoding to generate soft information, which will then be used to reconstruct and remove ITI for the adjacent tracks. At an AD of 3 Tb/in2 and a bit-error rate (BER) of 10<sup>-5</sup> , simulation results show that the proposed system achieves performance gains of 1 and 6.5 dB compared with the previously proposed 3H3T system and the conventional system with one-head one-track detection, respectively. In addition, our system demonstrates robust performance under challenging conditions, maintaining effectiveness with track mis-registration (TMR) up to 10% and media noise up to 5%. These results indicate that the proposed method can be considered as one of the promising solutions for ultrahigh-density BPMR systems.

Warisarn, Chanon

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