CAREER: Multitrack Read Channel Designs for Modern Two-Dimensional Magnetic Recording

职业：现代二维磁记录的多轨读取通道设计

• 批准号: 2238990
• 负责人: Elnaz Banan Sadeghian
• 金额: $ 55.05万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238990&HistoricalAwards=false
• 关键词: CAREER; Multitrack; Read; Channel; Designs

This project develops efficient noise prediction, synchronization, and symbol detection algorithms that support modern and future generations of ultra-high density two-dimensional magnetic recording. Two-dimensional magnetic recording is a novel technology in hard disk drives that allows a drastic increase in the data density to up to 10 Terabits per square inch, on already-existing head and media designs. This is achieved through shingled writing, where the adjacent data tracks are written with partial overlap on top of each other, like roof shingles, in order to squeeze many more tracks on the disk. Powerful signal processing algorithms enable efficient data recovery from highly interference-laden and noisy readback signals. This project investigates powerful signal processing algorithms, from purely communication-theoretic to machine learning models, and their combinations, for data recovery in such channels. The results of this project are expected to increase the data density of two-dimensional magnetic recording well beyond the current state-of-the-art. The project integrates an educational component in signal processing and communication theory in the form of 1) graduate student training, 2) research-oriented undergraduate student education, and 3) collaborative participation with industrial research.The novel elements of this project are that: 1) it adopts a multiple-input multiple-output (MIMO) model in accordance with the industry expectation for low-latency implementation; 2) it develops multitrack detection strategies as opposed to the current industry standard of single-track detection, in order to reach higher areal densities as well as throughput; and 3) in addition to considering all the channel impediments that are often considered separately in earlier works, this project also addresses the problem of timing asynchrony between the adjacent tracks and the analog-to-digital converter sampling rate. To achieve the above goals, four research thrusts will be pursued. In the first thrust, reduced-state redesigns of the trellis-based multitrack symbol detectors will be investigated. The second thrust develops media noise mitigation techniques for asynchronous multitrack detection. Here, both the MIMO extension of the pattern-dependent noise prediction algorithm and neural network noise predictor models will be studied. In the third thrust, deep neural network symbol detectors will be developed, both as a stand-alone joint symbol detector and synchronizer, as well as a joint symbol detector and synchronizer coupled with a low-density parity check decoder following the turbo detection mechanism. The fourth thrust develops novel read channels built entirely of neural networks, both as a separate network equalizer followed by a network symbol detector, and as a holistic deep neural network read channel.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.

该项目开发有效的噪声预测，同步和符号检测算法，支持现代和未来的超高密度二维磁记录。二维磁记录是硬盘驱动器中的一项新技术，在现有的磁头和介质设计上，它可以将数据密度大幅增加到每平方英寸10太比特。这是通过叠瓦式写入实现的，其中相邻的数据磁道在彼此顶部部分重叠地写入，如屋顶瓦，以便在磁盘上挤压更多的磁道。强大的信号处理算法可从高干扰和噪声的回读信号中实现高效的数据恢复。该项目研究了强大的信号处理算法，从纯粹的通信理论到机器学习模型，以及它们的组合，用于此类信道中的数据恢复。该项目的成果有望大大提高二维磁记录的数据密度，使其远远超过目前的最先进水平。该项目以1）研究生培养，2）以研究为导向的本科生教育，3）与工业研究合作参与的形式整合了信号处理和通信理论的教育部分。该项目的新颖元素是：1）它采用了符合行业期望的多输入多输出（MIMO）模型，以实现低延迟实现; 2）它开发了多轨道检测策略，而不是当前行业标准的单轨道检测，以达到更高的面密度以及吞吐量;以及3）除了考虑在早期工作中经常单独考虑的所有通道障碍物之外，该项目还解决了相邻轨道之间的定时延迟和模数转换器采样率的问题。为实现上述目标，将开展四项研究工作。在第一个推力，减少状态重新设计的网格为基础的多轨符号检测器将进行调查。第二个推力发展媒体噪声缓解技术异步多轨检测。在这里，将研究模式相关噪声预测算法和神经网络噪声预测器模型的MIMO扩展。在第三个推力中，将开发深度神经网络符号检测器，既作为独立的联合符号检测器和同步器，也作为联合符号检测器和同步器，与遵循turbo检测机制的低密度奇偶校验解码器耦合。第四个重点是开发完全由神经网络构建的新型读取通道，既作为一个单独的网络均衡器，后面是一个网络符号检测器，也作为一个整体的深度神经网络读取通道。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。