CRII: CIF: Machine Learning Based Equalization Towards Multitrack Synchronization and Detection in Two-Dimensional Magnetic Recording

该项目涉及二维磁记录(TDMR)，这是一种用于硬盘驱动器的新型记录技术，允许大幅提高数据密度，最高可达每平方英寸10太比特。来自TDMR的增益来自两个方向，即(I)瓦状写入机制，其中相邻数据轨道被部分重叠地写入，如屋顶瓦片，以便在盘上挤压更多轨道并增加数据密度，以及(Ii)强大的信号处理算法，其使得能够在数据轨道内和数据轨道之间都存在高水平干扰的情况下从噪声读回信号中高效地恢复数据。来自机器学习(ML)的技术将被用于在存在二维干扰、数据依赖和有色介质噪声的情况下开发这样的数据恢复算法。拟议的工作旨在实现TDMR的显著改进，最终允许在容量更大的更少磁盘驱动器上存储指数级增长的数据量。该奖项部分支持一名博士生接受TDMR读通道设计培训，最终为该学生在数据存储行业创造就业机会。研究的目标是开发高效的基于ML的均衡算法，其性能优于传统的通信理论均衡算法，用于高密度TDMR。TDMR信道是高度非线性的，ML方法有望更好地学习其特性，与传统的线性通信理论方案相比，可能会导致更高的误码率。所需的神经网络均衡方案寻求(I)结合介质噪声的预测和消除，以及(Ii)与由调查者开发的新的读通道体系结构兼容，该新的读通道体系结构将部分响应范例扩展到异步轨道的多轨道检测的情况。为了实现这种读取通道，在开发的均衡器之后将采用旋转目标(ROTAR)算法，这是一种异步轨道的多轨道检测器，也是调查者开发的。由此产生的读取通道预计将在面密度和吞吐量方面比目前行业中使用的通信理论和单轨检测方案产生收益。开发的算法的性能将使用国际合作者提供的现实波形与传统算法进行比较。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project concerns two-dimensional magnetic recording (TDMR), a novel recording technology for hard disk drives that allows for a drastic increase in data density, up to 10 terabits per square inch. Gains from TDMR come from two directions, namely (i) the shingled writing mechanism whereby adjacent data tracks are written with partial overlap, like roof shingles, in order to squeeze many more tracks on the disk and increase data density, and (ii) powerful signal processing algorithms that enable efficient data recovery from noisy readback signals in the presence of high levels of interference both within and across data tracks. Techniques from machine learning (ML) will be used in developing such data recovery algorithms in the presence of two-dimensional interference, and data-dependent and colored media noise. The proposed work aims to achieve significant improvements in TDMR, eventually allowing exponentially increasing volumes of data to be stored on fewer disk drives with higher capacities. This award partially supports a PhD student to be trained in TDMR read channel design, ultimately creating career opportunities for the student in the data storage industry. The research objective is the development of efficient ML based equalization algorithms that outperform conventional communication-theoretic equalization for high density TDMR. The TDMR channel being highly nonlinear, ML approaches are expected to better learn its characteristics, potentially leading to higher bit-error rates when compared to conventional linear communication-theoretic schemes. The desired neural network equalization schemes seek to (i) incorporate the prediction and cancellation of the media noise, and (ii) be compatible with a novel read channel architecture, developed by the investigator, that extends the partial-response paradigm to the case of multitrack detection of asynchronous tracks. To realize this read channel, the developed equalizers will be followed by the rotating-target (ROTAR) algorithm, a multitrack detector of asynchronous tracks, also developed by the investigator. The resulting read channel is expected to yield gains in areal density and throughput over the communication-theoretic and single-track detection schemes currently used in the industry. The performance of the developed algorithms will be compared against that of conventional algorithms using realistic waveforms provided by international collaborators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.