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

• Published in: IEEE transactions on magnetics Vol. 55; no. 11; pp. 1 - 5
• Main Authors: Luo, Ke, Wang, Shaobing, Chan, Kheong Sann, Chen, Wei, Chen, Jincai, Lu, Ping, Cheng, Weiming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TMAG.2019.2931760

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.