Sokjabok, Siwakon

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Sokjabok, Siwakon
Alternative Name
Sokjabok, S.
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Email
siwakon.so@kmitl.ac.th

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    Item type:Publication,
    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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    Item type:Publication,
    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.