期刊
IEEE TRANSACTIONS ON MAGNETICS
卷 55, 期 11, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2019.2931760
关键词
Error control coding; neural network; two-dimensional magnetic recording (TDMR); Voronoi grain media model
资金
- National Natural Science Foundation of China [61672246, 61272068, 61432007]
- Fundamental Research Funds for the Central Universities, HUST [2016YXMS018]
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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