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.

中文翻译：

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基于深度神经网络的多层磁记录检测和部分响应均衡

为了增加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回读波形由放置在上层上方的读取头恢复的来自各层的信号叠加而成。本文考虑了包括两层的MLMR的均衡和检测问题。为此，我们使用通过晶粒切换概率（GSP）模型生成的MLMR波形，该模型在现实的微磁模拟中进行了训练。我们提出了三种用于均衡和检测的系统。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）。我们证明了该系统优于传统的2-D线性最小均方误差（2-D-LMMSE）均衡器。第二个系统使用CNN对来自每一层的信号进行均衡和分离，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接口连接，我们表明，通过在一层系统上使用两层MLMR系统，可以实现16.2％的面密度增益。