CAREER: Timing Recovery in Digital Magnetic Recording Systems

随着磁记录密度的增加，将需要利用强大的信号处理算法来克服高达6dB的信噪比下降。最近，迭代译码方法（应用于Turbo码和低密度奇偶校验码）已被证明接近加性白色高斯噪声信道的信道容量。仿真结果表明，在磁记录部分响应信道中，Turbo码通常比传统的最大似然检测获得高达6dB的编码增益。迭代解码的一个缺点是判决延迟对于定时恢复环路来说是不可容忍的长。显然，需要能够在非常低的信噪比下操作并且能够容忍长判决延迟的鲁棒定时恢复算法。本项目正在为采用迭代解码策略的接收机开发定时恢复方法。由于迭代解码器引入了几千比特的延迟，因此建议对波形进行异步过采样，并且使用解码延迟来细化迭代调度中的采样，该迭代调度紧密地遵循迭代解码器的调度。本研究发展一种“软”决策导向的时序恢复方法。在迭代解码器的解码迭代期间，在处理元件之间传递软信息。该软信息是关于特定比特（或比特序列）的可靠性信息，并且可以用于细化接收波形的采样时刻。这正是这里使用的策略。迭代解码通过在码图上传递条件概率信息来很好地解释。最近，它已被证明，均衡和解码可以使用相同的消息传递策略上的联合信道/代码图执行。这里的目标是通过使用系统图来制定整个接收器来扩展这个概念。然后，在整个图上的合适的消息传递算法将执行基本的接收器功能（同步、采样、均衡、解码）。

As magnetic recording densities increase, powerful signal processing algorithms will need to be utilized to overcome a drop of up to 6dB in the signal-to-noise ratio. Recently, iterative decoding methods (applied to turbo codes and low-density parity-check codes) have shown to approach the channel capacity of additive white Gaussian noise channels. Simulation show that turbo codes routinely achieve coding gains up to 6dB over conventional maximum likelihood detection in magnetic recording partial response channels. A drawback of iterative decoding is a decision delay that is intolerably long for the timing recovery loop. Clearly, robust timing recovery algorithms that can operate at very low signal-to-noise ratios and can tolerate long decision delays are needed. This project is developing timing recovery methods for receivers employing iterative decoding strategies. Since iterative decoders introduce a delay of several thousand bits, it is suggested that the waveform be asynchronously oversampled, and that the decoding delay be used to refine the sampling in an iterative schedule that follows closely the schedule of the iterative decoder. This research developing a 'soft' decision-directed timing recovery method. During the decoding iterations of an iterative decoder, soft information is passed between the processing elements. This soft information is the reliability information regarding a specific bit (or a bit-sequence) and could be used to refine the sampling instants of the received waveform. This is precisely the strategy used here. Iterative decoding is well explained by passing conditional probability information on a code-graph. Recently, it has been shown that both equalization and decoding can be performed using the same message-passing strategy on a joint channel/code graph. The goal here is to expand this concept by formulating the entire receiver using a system graph. A suitable message-passing algorithm on the entire graph will then perform the basic receiver functions (synchronization, sampling, equalization, decoding).