A Study on Block-Based Neural Network Equalization in TDMR System With LDPC Coding

To achieve a high track density, two-dimensional magnetic recording (TDMR) is combined with shingled magnetic recording (SMR). SMR makes it possible to record 1 bit on a few grains. However, the performance will be remarkably deteriorated by the increased media noise, the inter-track and inter-symbo...

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Vydáno v:IEEE transactions on magnetics Ročník 55; číslo 11; s. 1 - 5
Hlavní autoři: Luo, Ke, Wang, Shaobing, Chan, Kheong Sann, Chen, Wei, Chen, Jincai, Lu, Ping, Cheng, Weiming
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.11.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial neural networks
Binary system
Bit error rate
Codes
Coding
Computer simulation
Density
Encoding
Equalization
Equalizers
Error control coding
Error correcting codes
Magnetic recording
Magnetism
Media
neural network
Neural networks
Parity check codes
Signal to noise ratio
two-dimensional magnetic recording (TDMR)
Voronoi grain media model
ISSN:0018-9464, 1941-0069
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Shrnutí:To achieve a high track density, two-dimensional magnetic recording (TDMR) is combined with shingled magnetic recording (SMR). SMR makes it possible to record 1 bit on a few grains. However, the performance will be remarkably deteriorated by the increased media noise, the inter-track and inter-symbol interference (ITI and ISI). Therefore, the application of effective equalization and error control coding are required. In this paper, we investigate a simple block-based neural network equalizer (NNE) that mitigates the influence of ITI and ISI. We compare the equalization effects of the NNE and a conventional 2-D equalizer with low-density parity-check (LDPC) coding based on a random Voronoi grain media model. Simulation results show the proposed block-based NNE achieves better bit error rate performance than the conventional 2-D linear equalizer followed by the a posteriori probability (APP) detector and a sum-product (SP) decoder. In addition, we find the block-based NNE is sensitive to write errors.
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ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2019.2931760