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R&D: Convolutional Neural Network-Based Media Noise Prediction and Equalization for TDMR Turbo-Detection with Write/Read TMR

Simulation results employing realistic grain switching probabilistic (GSP) media model show that proposed system is quite robust to track-misregistration (TMR).

IEEE Xplore has published, in 2022 IEEE 33rd Magnetic Recording Conference (TMRC) proceedings, an article written by Amirhossein Sayyafan, Ahmed Aboutaleb, Benjamin Belzer, Krishnamoorthy Sivakumar, Washington State University, Pullman, WA, USA, and Simon Greaves, RIEC, Tohoku University, Sendai, Japan.

Abstract: This paper presents a turbo-detection system consisting of a convolutional neural network (CNN) based equalizer, a Bahl-Cocke-Jelinek-Raviv (BCJR) trellis detector, a CNN-based media noise predictor (MNP), and a low-density parity-check (LDPC) channel decoder for two-dimensional magnetic recording (TDMR). The BCJR detector, CNN MNP, and LDPC decoder iteratively exchange soft information to maximize the areal density (AD) subject to a bit error rate (BER) constraint. Simulation results employing a realistic grain switching probabilistic (GSP) media model show that the proposed system is quite robust to track-misregistration (TMR). Compared to a I-D pattern-dependent noise prediction (PDNP) baseline with soft intertrack interference (ITI) subtraction, the system achieves 0.34% AD gain with read-TMR alone and 0.69% with write- and read-TMR together.

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