A study on usefulness of neural network equalizer to reduce impact of amplitude fluctuations in magnetic tape
Year: 2024
Type: article
Abstract: As digital data grows explosively year after year, magnetic tape devices, with their high reliability and long-term storage capacity, are widely used for backup and archiving. Magnetic tape devices with larger capacity are required, and it is necessary to improve track density. We have previously constructed a simulator that considers inter-track interference (ITI) due to narrower tracks in barium ferrite (BaFe) magnetic tape drives and evaluated its performance. In this study, we develop a simulator which more accurately models the experimental data, considering amplitude fluctuations caused by the problem of poor tape running, and discuss the usefulness of a neural network equalizer to reduce the effects of amplitude fluctuations. As a result, we show that applying the neural network equalizer is useful in reducing the impact of amplitude fluctuations caused by the problem of poor tape running.
Authors: Madoka Nishikawa, Yasuaki Nakamura, Yoshihiro Okamoto
Institution: Ehime University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,210
OpenAlex ID: W4402910952