1. Eliminating Media Noise While Preserving Storage Capacity: Reconfigurable Constrained Codes for Two-Dimensional Magnetic Recording
- Author
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Guzel, Iven, Özbayrak, Doğukan, Calderbank, Robert, and Hareedy, Ahmed
- Subjects
Computer Science - Information Theory ,Electrical Engineering and Systems Science - Signal Processing - Abstract
Magnetic recording devices are still competitive in the storage density race with solid-state devices thanks to new technologies such as two-dimensional magnetic recording (TDMR). Advanced data processing schemes are needed to guarantee reliability in TDMR. Data patterns where a bit is surrounded by complementary bits at the four positions with Manhattan distance $1$ on the TDMR grid are called plus isolation (PIS) patterns, and they are error-prone. Recently, we introduced lexicographically-ordered constrained (LOCO) codes, namely optimal plus LOCO (OP-LOCO) codes, that prevent these patterns from being written in a TDMR device. However, in the high-density regime or the low-energy regime, additional error-prone patterns emerge, specifically data patterns where a bit is surrounded by complementary bits at only three positions with Manhattan distance $1$, and we call them incomplete plus isolation (IPIS) patterns. In this paper, we present capacity-achieving codes that forbid both PIS and IPIS patterns in TDMR systems with wide read heads. We collectively call the PIS and IPIS patterns rotated T isolation (RTIS) patterns, and we call the new codes optimal T LOCO (OT-LOCO) codes. We analyze OT-LOCO codes and present their simple encoding-decoding rule that allows reconfigurability. We also present a novel bridging idea for these codes to further increase the rate. Our simulation results demonstrate that OT-LOCO codes are capable of eliminating media noise effects entirely at practical TD densities with high rates. To further preserve the storage capacity, we suggest using OP-LOCO codes early in the device lifetime, then employing the reconfiguration property to switch to OT-LOCO codes later. While the point of reconfiguration on the density/energy axis is decided manually at the moment, the next step is to use machine learning to take that decision based on the TDMR device status., Comment: 15 pages (double column), 11 figures, submitted to the IEEE Transactions on Magnetics (TMAG). arXiv admin note: text overlap with arXiv:2010.10686
- Published
- 2023