Study of CNN-Based Data Detection in Dual-Layer Bit-Patterned Magnetic Recording Systems

Simple item page
dc.contributor.authorSangthong, Siraphop
dc.contributor.authorSokjabok, Siwakon
dc.contributor.authorKhametong, Anawin
dc.contributor.authorWarisarn, Chanon
dc.date.accessioned2026-08-06T10:49:08Z
dc.date.available2026-08-06T10:49:08Z
dc.date.issued2025-01-01
dc.description.abstractThis 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.
dc.identifier.citation2025 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2025, 2025
dc.identifier.doi10.1109/ITC-CSCC66376.2025.11137650
dc.identifier.other2-s2.0-105016400012
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16442
dc.source2025 International Technical Conference on Circuits Systems Computers and Communications Itc Cscc 2025
dc.subjectBit-patterned magnetic recording
dc.subjectCNN
dc.subjectData detection
dc.subjectDual-layer recording
dc.subjectSliding window technique
dc.titleStudy of CNN-Based Data Detection in Dual-Layer Bit-Patterned Magnetic Recording Systems
dc.typeConference Paper

Files

Collections

Research