A perspective on deep neural network-based detection for multilayer magnetic recording
A perspective on deep neural network-based detection for multilayer magnetic recording
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DOI：
10.1063/5.0051085
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发表时间：
2021-07
影响因子：
4
通讯作者：
Ahmed Aboutaleb;Amirhossein Sayyafan;K. Sivakumar;B. Belzer;S. Greaves;K. Chan;R. Wood
中科院分区：
文献类型：
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作者：
Ahmed Aboutaleb;Amirhossein Sayyafan;K. Sivakumar;B. Belzer;S. Greaves;K. Chan;R. Wood
本文描述了多层磁记录（MLMR）中数据恢复的挑战、解决方案和前景-垂直堆叠磁介质层以增加信息存储密度。为此，MLMR的信道模型进行了讨论。数据恢复描述的读回阶段，其次是均衡，然后检测。我们说明了深度神经网络（DNN）如何用于设计MLMR均衡和检测系统。我们表明，这种基于DNN的系统优于传统的基线，并提供了一个很好的复杂性和性能之间的权衡。为了实现额外的密度增益，几个前瞻性的方法进行了讨论。在物理层面上，可以通过谐振阅读来实现对不同层上的轨道的选择性阅读。谐振阅读保证减少来自不同层的干扰，从而实现更高的存储密度。关于信号处理，DNN可以用于估计媒体噪声并与解码器迭代地交换软比特信息。此外，为了改善部分擦除，提出了一种基于自动编码器的系统作为调制编码方案。
This paper describes challenges, solutions, and prospects for data recovery in multilayer magnetic recording (MLMR)—the vertical stacking of magnetic media layers to increase information storage density. To this end, the channel model for MLMR is discussed. Data recovery is described in terms of the readback stage followed by equalization and then detection. We illustrate how deep neural networks (DNNs) can be used to design systems for equalization and detection for MLMR. We show that such DNN-based systems outperform the conventional baseline and provide a good trade-off between complexity and performance. To achieve additional density gains, several prospective methods are discussed. On a physical level, the selective reading of tracks on different layers can be achieved by resonant reading. Resonant reading promises reduced interference from different layers, enabling higher storage densities. Regarding the signal processing, DNNs can be used to estimate the media noise and iteratively exchange soft-bit information with the decoder. Also, to ameliorate partial erasures, an auto-encoder-based system is proposed as a modulation coding scheme.