Deep Neural Network-based Detection and Partial Response Equalization for Multilayer Magnetic Recording
Deep Neural Network-based Detection and Partial Response Equalization for Multilayer Magnetic Recording
复制标题
基于深度神经网络的多层磁记录检测和部分响应均衡
DOI：
10.1109/tmrc49521.2020.9366719
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
WOOD, Roger
中科院分区：
文献类型：
--
作者：
ABOUTALEB, Ahmed;SAYYAFAN, Amirhossein;BELZER, Benjamin;SIVAKUMAR, Krishnamoorthy;GREAVES, Simon;CHAN, Kheong;WOOD, Roger
为了提高磁记录通道的存储容量限制，最近的研究提出了多层磁记录（MLMR）：磁介质层的垂直堆叠。 MLMR 读回波形由放置在上层上方的读头恢复的每层信号的叠加组成。本文考虑了包含两层的 MLMR 的均衡和检测问题。为此，我们使用经过实际微磁模拟训练的晶粒切换概率 (GSP) 模型生成的 MLMR 波形。我们提出了三种均衡和检测系统。第一个是卷积神经网络 (CNN) 均衡器，然后是用于检测的 MLMR 维特比算法 (VA)。我们证明该系统优于传统的二维线性最小均方误差 (2-D-LMMSE) 均衡器。第二个系统使用 CNN 来均衡和分离每层的信号，然后是常规 VA。第三个系统包含经过训练可直接提供软比特估计的 CNN。通过将 CNN 检测器与通道解码器连接，我们表明，与一层系统相比，两层 MLMR 系统可以实现 16.2% 的面密度增益。
To increase the storage capacity limit of magnetic recording channels, recent studies proposed multilayer magnetic recording (MLMR): the vertical stacking of magnetic media layers. MLMR readback waveforms consist of the superposition of signals from each layer recovered by a read head placed above the upper layer. This article considers the problem of equalization and detection for MLMR comprising two layers. To this end, we use MLMR waveforms generated using a grain switching probability (GSP) model that is trained on realistic micromagnetic simulations. We propose three systems for equalization and detection. The first is a convolutional neural network (CNN) equalizer followed by an MLMR Viterbi algorithm (VA) for detection. We show that this system outperforms the traditional 2-D linear minimum mean squared error (2-D-LMMSE) equalizer. The second system uses CNNs for equalization and separation of signals from each layer, which is followed by a regular VA. The third system contains CNNs trained to directly provide soft bit estimates. By interfacing the CNN detector with a channel decoder, we show that an areal density gain of 16.2% can be achieved by a two-layer MLMR system over a one-layer system.
3-D-MAMR 媒体堆栈的优化
影响因子：
2.1
作者：
K. Chan;S. Greaves;S. Rahardja
通讯作者：
S. Rahardja
影响因子：
2.1
作者：
Chan, Kheong Sann;Aboutaleb, Ahmed;Sivakumar, Krishnamoorthy;Belzer, Benjamin;Wood, Roger;Rahardja, Susanto
通讯作者：
Rahardja, Susanto