Bidirectional Pattern-Dependent Noise Prediction With LDPC Codes for HAMR

• Published in: IEEE transactions on magnetics Vol. 49; no. 6; pp. 2661 - 2664
• Main Authors: Yibin Ng, Kui Cai, Vijaya Kumar, B. V. K., Zhiliang Qin, Tow Chong Chong
• Format: Journal Article Conference Proceeding
• Language: English
• Published: New York, NY IEEE 01.06.2013; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cross-disciplinary physics: materials science; rheology; Detectors; Exact sciences and technology; Heat-assisted magnetic recording; Information storage; Jitter; jitter noise; Magnetism; Materials science; Noise; noise prediction; Other topics in materials science; Parity check codes; pattern-dependent noise; Physics; Signal to noise ratio; Williams-Comstock recording model; noise prediction; pattern-dependent noise; Prediction; Heat-assisted magnetic recording; jitter noise; Modelling; Williams-Comstock recording model; Magnetic recording; Magnetic properties
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2013.2255267

As areal densities increase, substantial jitter noise is expected in heat-assisted magnetic recording (HAMR). To mitigate the effects of jitter noise, in an earlier work we proposed the bidirectional pattern-dependent noise prediction (BiPDNP) detector, which employs backward linear prediction as well as the conventional forward linear prediction. However, no error correction codes were used in this earlier work. In this paper, we implement BiPDNP in the Bahl-Cocke-Jelinek-Raviv (BCJR) detector, and investigate its performance with low-density parity check (LDPC) codes. For the LDPC coded channel, by combining the BCJR detector with BiPDNP, we observe that a SNR gain of 1 dB (at bit error rate 10 -4 with 30% microtrack jitter) is achieved over the conventional BCJR detector. Further, in HAMR channel modeling we employ the thermal Williams-Comstock (TWC) model. Conventionally, a linear relationship for coercivity with temperature is used. In this paper, we update the TWC model by using a nonlinear relationship for coercivity with temperature.