Abstract
Dynamic bit encoding and decoding of the magnetic recording process remain a challenge in that the process is restrained by the balance between reading and writing performance of the decoder’s bit error rate (BER). Sequential neural networks offer data streamflow for processes to reproduce recoded bits from signal distribution, overcoming the limitation of codeword mapping designed for each specific bit-patterned magnetic recording (BPMR) channel. Here, we implement the vanilla long short-term memory (LSTM) for adaptive modulation decoders in various BPMR channel designs within a single network, which benefits multi-channel decoder calibration tools with the same standardization. Signal information from media readback, a two-dimensional (2D) equalizer, 2D Viterbi, and a 2D soft-output Viterbi algorithm (SOVA) detector is arranged as a tensor that enables sequence-to-sequence bit prediction even with a highly complex data arrangement. Our adaptive model can predict recorded bits from readback with accuracies of approximately 97% for rate 4/5 decoding and 75% for crossing platforms, using a recently proposed single-reader/two-track reading (SRTR) system at an areal density of 4 Tb/in2 in a signal-to-noise ratio range of 1 to 8 dB. We conducted a BER simulation with the relevant results from conventional decoders and the LSTM model. Ultimately, our approach may demonstrate the limitation of supervised learning designed for BPMR systems and reveal a sequence data focus on LSTM that paves the way for sequential-type, unsupervised, mechanism-based, next-generation magnetic recordings.
Highlights
As a high-storage-density technology within a compact containment, magnetic recording technology has rapidly developed and paved the way to new bit encode-decode, media and material fabrication techniques [1]–[2]
Since readback media signals relate to a crucial channel element for minimizing bit error rates (BER), a sufficient technique for high-precision decoding channels with multiple modulation code rates maintains a pivotal role in the future of magnetic recording platforms [5]
In the first bit-patterned media recording (BPMR) on rate-4/5 modulation code, real training was consistent with readback signal, 2D equalizer, 2D Viterbi, and 2D soft-output Viterbi algorithm (SOVA) from random bit patterned media simulations in which bit island position uncertainty was very close to the real media
Summary
As a high-storage-density technology within a compact containment, magnetic recording technology has rapidly developed and paved the way to new bit encode-decode, media and material fabrication techniques [1]–[2]. We present a transparent LSTM application for modulation decoders that can condense AWGN, ISI, and ITI effects and learns to interpret any signal in reading channels, overcoming the limitation of all BPMR channels that rely on codeword mapping This approach offers benefits as a tool to calibrate all proposed channel systems with the same standardization as real BPMR media in the near future. Instead of mapping a codeword to minimize noise, we address the LSTM strategy to reveal an overall signal distribution throughout an entire sector by every read channel With this application, neural networks can learn any channel processing across different platforms via backup training for desired signals within a backend channel in order to adapt to such a system. The configuration of the sequence-to-sequence channel surpasses a signal level at each timestep with only user bit recording, which includes ISI, ITI, media, and electronic interferences
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