Kankhunthod, Kittipon
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Preferred name
Kankhunthod, Kittipon
Alternative Name
Kankhunthod, K.
Main Affiliation
Email
kittipon.ka@kmitl.ac.th
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Item type:Publication, Reliability Test Techniques in Tabu Search Detection for Enhancing BER Performance of Array Reader Bit-Patterned Magnetic Recording Systems(2026-01-01); ; Bit-patterned magnetic recording (BPMR) at ultra-high densities is strongly affected by inter-track interference (ITI). To cope with severe ITI, we introduce a reliability-testing mechanism with adaptive symmetric thresholding based on the distribution of log-likelihood ratios (LLRs) to effectively identify unreliable bits while controlling the computational complexity of the Tabu search (TS) detector. Additionally, the selected bits identified from the TS detection are employed to refine the original LLR values through a proposed soft-information adjustment (SIA) process. Moreover, we also present an LLR weighting scheme to further enhance the refined LLRs produced by the SIA process, thereby improving the performance of low-density parity-check decoding. Results indicate that our proposed technique can reduce the complexity of the TS detector by using a reliability-testing mechanism. The SIA can be effectively combined with an LLR weighting scheme, thereby improving bit-error rate performance over conventional BPMR systems. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Track Misregistration Mitigation Using CNN-Based Method on Single-Reader/Two-Track Reading BPMR Systems(2023-01-01); 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 single-reader/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.