Multitrack Detection with 2D Pattern-Dependent Noise Prediction
Multitrack Detection with 2D Pattern-Dependent Noise Prediction
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具有 2D 模式相关噪声预测的多轨检测
DOI：
10.1109/icc.2018.8422905
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
J. Barry
中科院分区：
文献类型：
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作者：
Shanwei Shi;J. Barry
磁记录中多个读取器的出现为多磁道检测打开了大门，在多磁道检测中，多个磁道被联合检测。多磁道检测是跨磁道编码（包括调制和差错控制码）和交叉磁道噪声预测的关键使能器，这两者都不能使用单磁道检测器充分利用。在本文中，我们提出了二维模式相关的噪声预测（2D-PDNP）算法作为一个解决方案的联合最大似然多轨道检测问题，面对模式相关的自回归高斯噪声。该解决方案采用网格上的维特比算法的形式，该网格对信道和噪声的组合记忆进行建模，其中分支度量可以被解释为2D模式相关的噪声预测，其中考虑到在下行和交叉方向上发生的转变，在下行和交叉方向上预测噪声。数值结果表明，在一组准微磁模拟通道波形与一对阅读器和一个多轨道检测器同时检测两个轨道，2D-PDNP算法提供了4%的面密度增加。
The advent of multiple readers in magnetic recording opens the door to multitrack detection, in which multiple tracks are detected jointly. Multitrack detection is a key enabler for both coding across tracks (including modulation and error-control codes) and crosstrack noise prediction, neither of which can be fully exploited using single-track detectors. In this paper, we propose the two-dimensional pattern- dependent noise-prediction (2D-PDNP) algorithm as a solution to the joint maximum-likelihood multitrack detection problem in the face of pattern-dependent autoregressive Gaussian noise. The solution takes the form of the Viterbi algorithm over a trellis that models the combined memory of the channel and noise, with a branch metric that can be interpreted as 2D pattern- dependent noise prediction, where noise is predicted in both downtrack and crosstrack directions, taking into account transitions occurring in both downtrack and crosstrack directions. Numerical results show that, on a set of quasi-micromagnetic simulated channel waveforms with a pair of readers and a multitrack detector detecting two tracks simultaneously, the 2D-PDNP algorithm provides a 4% increase in areal density.