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首页> 外文期刊>Magnetics, IEEE Transactions on >Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR
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Generalized Partial Response Equalization and Data-Dependent Noise Predictive Signal Detection Over Media Models for TDMR

机译：TDMR媒体模型上的广义局部响应均衡和数据相关的噪声预测信号检测

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摘要

Two-dimensional magnetic recording (2-D TDMR) is an emerging technology that aims to achieve areal densities as high as 10 Tb/in using sophisticated 2-D signal-processing algorithms. High areal densities are achieved by reducing the size of a bit to the order of the size of magnetic grains, resulting in severe 2-D intersymbol interference (ISI). Jitter noise due to irregular grain positions on the magnetic medium is more pronounced at these areal densities. Therefore, a viable read-channel architecture for TDMR requires 2-D signal-detection algorithms that can mitigate 2-D ISI and combat noise comprising jitter and electronic components. Partial response maximum likelihood (PRML) detection scheme allows controlled ISI as seen by the detector. With the controlled and reduced span of 2-D ISI, the PRML scheme overcomes practical difficulties such as Nyquist rate signaling required for full response 2-D equalization. As in the case of 1-D magnetic recording, jitter noise can be handled using a data-dependent noise-prediction (DDNP) filter bank within a 2-D signal-detection engine. The contributions of this paper are threefold: 1) we empirically study the jitter noise characteristics in TDMR as a function of grain density using a Voronoi-based granular media model; 2) we develop a 2-D DDNP algorithm to handle the media noise seen in TDMR; and 3) we also develop techniques to design 2-D separable and nonseparable targets for generalized partial response equalization for TDMR. This can be used along with a 2-D signal-detection algorithm. The DDNP algorithm is observed to give a 2.5 dB gain in SNR over uncoded data compared with the noise predictive maximum likelihood detection for the same choice of channel model parameters to achieve a channel bit density of 1.3 Tb/in with media grain center-to-center distance of 10 nm. The DDNP algorithm is observed to give 10%- gain in areal density near 5 grains/bit. The proposed signal-processing framework can broadly scale to various TDMR realizations and areal density points.
机译：二维磁记录（2-D TDMR）是一种新兴技术，旨在使用复杂的2-D信号处理算法实现高达10 Tb / in的面密度。通过将位的大小减小到磁粒的大小，可以实现较高的面密度，从而导致严重的2D符号间干扰（ISI）。在这些面密度下，由磁性介质上不规则的晶粒位置引起的抖动噪声更为明显。因此，可行的TDMR读取通道架构需要2D信号检测算法，该算法可以减轻2D ISI并消除包括抖动和电子组件的噪声。部分响应最大似然（PRML）检测方案允许检测器看到受控的ISI。通过2-D ISI的受控且减小的跨度，PRML方案克服了实际困难，例如完全响应2-D均衡所需的奈奎斯特速率信令。与一维磁记录的情况一样，可以使用二维信号检测引擎中的数据相关噪声预测（DDNP）滤波器组来处理抖动噪声。本文的贡献有三点：1）我们使用基于Voronoi的颗粒介质模型对TDMR中的抖动噪声特性进行了实证研究，该抖动特性是晶粒密度的函数。 2）我们开发了一种二维DDNP算法来处理TDMR中出现的介质噪声；和3）我们还开发了设计TDMR的广义部分响应均衡的二维可分离和不可分离目标的技术。可以将其与2-D信号检测算法一起使用。对于相同选择的信道模型参数，在媒体颗粒中心到中心达到1.3 Tb / in的信道位密度的情况下，与噪声预测最大似然检测相比，DDNP算法在未编码数据上的SNR增益为2.5 dB。中心距离为10 nm。观察到DDNP算法在5粒/位附近的面密度上增加了10％。所提出的信号处理框架可以广泛地扩展到各种TDMR实现和面密度点。

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