Convolutional Neural Network-Based Media Noise Prediction and Equalization for TDMR Turbo-Detection With Write/Read TMR

• Published in: IEEE transactions on magnetics 2023-03, Vol.59 (3), p.1-11
• Main Authors: Sayyafan, Amirhossein, Aboutaleb, Ahmed, Belzer, Benjamin J., Sivakumar, Krishnamoorthy, Greaves, Simon, Chan, Kheong Sann, James, Ashish
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Bahl-Cocke-Jelinek-Raviv (BCJR) detector; CNN equalizer; CNN media noise predictor (MNP); Codes; convolutional neural network (CNN); Convolutional neural networks; Data models; Datasets; Decoding; deep neural network (DNN); Detectors; Equalizers; Error correcting codes; low-density parity-check (LDPC) decoder; Magnetic recording; Magnetism; Media; Neural networks; Noise prediction; Parity check codes; Random processes; Sensors; turbo-detection system; two-dimensional magnetic recording (TDMR); Waveforms; write/read track misregistration (TMR)
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2022.3216640

This article considers a turbo-detection system that includes 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) in the presence of track misregistration (TMR). The input readings are passed to a 2-D partial response (PR) equalizer, which is either linear or CNN-based. The equalized waveforms are inputs to a 2-D BCJR detector, which generates log-likelihood-ratio (LLR) outputs. The CNN MNP is provided with BCJR LLRs to estimate signal-dependent media noise samples and feed them back to the BCJR. A second pass through the BCJR produces LLRs, which are decoded by an LDPC decoder; achieved areal density (AD) is computed from the LDPC code rate. Spatially varying read- and write-TMR models are developed. We investigate the performance of the proposed system on simulated TDMR readback waveforms generated by grain-switching probabilistic (GSP) simulations. We have two types of GSP datasets. Dataset #1 includes two 10 nm bit length (BL) datasets with 18 and 24 nm track pitch (TP). Dataset #2 has 11 nm BL and 15 nm TP. The comparison baseline is a 1-D BCJR detector with pattern-dependent noise prediction (PDNP) and soft intertrack interference (ITI) subtraction, referred to as 1-D PDNP with LLR exchange. The write-TMR and read-TMR are modeled as cross-track-independent downtrack-correlated random processes. In the presence of joint write- and read-TMR, the proposed turbo-detection system achieves 8.34% and 0.70% AD gain over 1-D PDNP with LLR exchange for TP 18 and 24 nm dataset #1, respectively, and is more robust to TMR compared to the baseline.