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Three-Track Detection Using a Multilayer Perceptron for Dual-Layer Bit-Patterned Magnetic Recording Systems
(2026-01-01)
Koonkarnkhai, Santi
;
Plotchu, Siriphon
;
Martnok, Warunee
;
Rueangnetr, Natthakan
;
Kilaso, Sathapath
This article proposes a multilayer perceptron (MLP)-based three-track detection method for dual-layer bit-patterned magnetic recording (BPMR) systems. Three different MLP architectures are explored and evaluated, namely: 1) a single MLP detecting all three tracks simultaneously; 2) three MLPs, each detecting one track independently; and 3) two MLPs dedicated to upper and lower recording layers. Simulation results show that the proposed MLP-based systems outperform the conventional partial-response maximum-likelihood (PRML) detection scheme, particularly under severe interferences and high areal density (AD). Among the proposed systems, the two-MLP architecture offers the optimal balance between detection accuracy and computational complexity, making it the most promising solution for future high-density magnetic recording systems.
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Inter-Layer Interference (ILI) Suppression in Dual-Layer Bit-Patterned Magnetic Recording Systems
(2026-01-01)
Rueangnetr, Natthakan
;
Koonkarnkhai, Santi
;
Greaves, Simon John
;
Warisarn, Chanon
Dual-layer bit-patterned magnetic recording (DL-BPMR) systems are promising for achieving higher areal densities (ADs). However, they face significant challenges, including inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI). To address these issues, this work proposes integrating a sum-soft-information (SSI) technique and an ILI suppression method to enhance detection reliability. The SSI technique is initially used to improve the reliability of the log-likelihood ratio (LLR) for the bottom layer signal by leveraging the mutual information derived from a staggered array reader configuration. The enhanced data sequence of the bottom layer is subsequently utilized to suppress ILI by applying a weighting before being removed from the mixed readback signal. The separated readback signal of the top layer is then processed using well-predesigned equalizers and detectors. Simulation results demonstrate that the proposed method significantly improves bit error rate (BER) performance compared to conventional single-layer and DL-BPMR systems, particularly at a user density of 4.0 Tb/in2, making it a promising approach for next-generation high-density magnetic recording.
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Enhancing Log-Likelihood Ratios with Mutual Information on Three-Reader One-Track Detection in Staggered BPMR Systems
(2025-03-01)
Rueangnetr, Natthakan
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
;
Greaves, Simon John
;
Warisarn, Chanon
Because so much information is currently being shared online, there has been a sharp rise in the need for data storage devices over the past ten years. The main storage option is the hard disk drive (HDD), which is less expensive than some other types of data storage. Physical constraints, such as the superparamagnetic limit, are difficult to overcome using existing HDD technology. Consequently, bit-patterned magnetic recording (BPMR) has emerged as a potential solution, offering higher areal densities whilst maintaining thermal stability. Nevertheless, BPMR poses new challenges, such as inter-symbol interference and inter-track interference. Consequently, a number of approaches, such as staggered island layouts and array-reader magnetic recording, have been proposed to overcome these issues. However, this article proposes a three-reader one-track detection method to enhance data retrieval in a staggered BPMR system. Leveraging three-track reading for one-track detection, we obtain three readback signals that function as mutual data sequences. This substantially enhances the detection process in one-dimensional partial-response maximum-likelihood channels. Next, using these mutual data sequences, four novel techniques are presented to enhance bit-error rate (BER) performance and detection accuracy: hard-information flipping, maximum soft-information finding, bit-summation detection, and multilayer perceptron (MLP). This study shows that these proposed techniques can provide better BER performance compared with conventional methods and that the MLP is the most effective technique in enhancing system performance.
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Inter-Layer Interference (ILI) Suppression in Dual-Layer Bit-Patterned Magnetic Recording Systems
(2025-01-01)
Rueangnetr, Natthakan
;
Koonkarnkhai, Santi
;
Greaves, Simon John
;
Warisarn, Chanon
Dual-layer bit-patterned magnetic recording (DL-BPMR) systems are promising for achieving higher areal densities. However, they face significant challenges, including inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI). To address these issues, this work proposes integrating a sum-soft-information (SSI) technique and an ITI suppression method to enhance detection reliability. The SSI technique is initially used to improve the reliability of the log-likelihood ratio for the bottom layer signal by leveraging the mutual information derived from a staggered array reader configuration. The enhanced data sequence from the bottom layer is subsequently utilized to suppress ILI by applying a weighting before it is subtracted from the top layer readback signals. Simulation results demonstrate that the proposed method significantly improves bit error rate (BER) performance compared to conventional single-layer and dual-layer BPMR systems, particularly at a user density of 4.0 Tb/in2, making it a promising approach for next-generation high-density magnetic recording.
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Three-Track Detection Using a Multi-Layer Perceptron for Dual-Layer Bit-Patterned Magnetic Recording Systems
(2025-01-01)
Koonkarnkhai, Santi
;
Plotchu, Siriphon
;
Martnok, Warunee
;
Rueangnetr, Natthakan
;
Kilaso, Sathapath
This paper proposes a multi-layer perceptron (MLP)-based three-track detection method for dual-layer bit-patterned magnetic recording systems. Three architectures are explored: one MLP for three tracks, two MLPs for upper and lower layers, and three individual MLPs per track. Simulation results show that all MLP-based methods outperform conventional partial response maximum likelihood detection, especially under high areal density and complex interference. Among them, the two-MLP system achieves the best bit-error rate performance by effectively separating detection tasks across layers.
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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 10-5 , 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.
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An MLP-based skew angle mitigation method for bit-patterned magnetic recording
(2024-06-01)
Koonkarnkhai, Santi
;
Kankhunthod, Kittipon
;
Kovintavewat, Piya
Skew angle (SA) is one of the crucial problems in a bit-patterned magnetic recording (BPMR) system. Practically, during the read process, the read head may be tilted up to 30 degrees (◦) away from the target track. Without the SA detection and correction mechanism, the performance of the BPMR system will be unbearable. This paper proposes a SA mitigation strategy for a BPMR system based on a multilayer perceptron (MLP) so as to estimate and rectify the SA. Specifically, the MLP-based SA estimator extracts the SA amount directly from the readback signal, which will then be used in the MLP-based detector to produce the estimated recording bits. Simulation results show that the proposed method can estimate the SA embedded in the readback signal more precisely than the conventional one, which just uses a look-up table to detect the SA from the target coefficients. Particularly, for the system operating at an areal density (AD) of 3 terabits per square inch with SA = 15◦ and 30◦, the suggested system performs better than the system without the SA mitigation method.
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Neural Networks Input Techniques to Maintain a Small Skew Angle in Bit-Patterned Magnetic Recording with a V-Shaped Read-Head Array
(2023-01-01)
Fatika, Kirana Alif
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Koonkarnkhai, Santi
;
Kovintavewat, Piya
;
Warisarn, Chanon
The demand for enormous storage devices has kept increasing, leading to the development of various advanced technologies with a vast storage capacity. Extensive numbers of related research studies have been aiming at optimizing code design and algorithms analytically; however, enacting them on practical devices has been scarce. Achieving this demand might bring some obstacles called two-dimensional interference and skew angle (SA). To meet the challenge of the obstacle, we propose a SA detection method for bit-patterned magnetic recording systems by computing a specific target by three readback sequences before estimating the SA value and detecting the SA amount happening in the system using an application of neural network namely multilayer perceptron. An error correction code, low-density parity-check, is applied, and its decoder outputs a log-likelihood ratio whose probability density distribution is examined. The simulation results show that the sliding window technique can significantly provide a better bit error rate performance.
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A Linear Support Vector Machine Based Detector for Bit-Patterned Magnetic Recording
(2023-01-01)
Khametong, Anawin
;
Koonkarnkhai, Santi
;
Kovintavewat, Piya
;
Warisarn, Chanon
The demand for high-capacity storage devices for storing digital information is continuously increasing because of the rapid growth in the number of social media users. Alternative magnetic recording technologies, such as bit-patterned magnetic recording (BPMR), have been proposed in parallel with the current perpendicular magnetic recording technology. However, to increase the areal density in BPMR, we unavoidably encounter the problems of two-dimensional (2D) interference and track mis-registration (TMR). Consequently, to solve these problems, we first present the modified soft-information adjuster (SIA) to mitigate the 2D interference and improve the log-likelihood ratios (LLRs) that were initially produced from the conventional detectors. Then, we propose a linear support vector machine (LSVM)-based detector that works with the modified SIA so as to enhance the reliability of LLRs, which can in turn provide better estimated user bits. Simulation results reveal that the proposed system can yield better bit-error rate performance and is more robust to the TMR effect than the conventional system without the LSVM-based detector.
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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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