Experimental validation of the triangle zig-zag transition model

Recently, work has been published on a channel model that employs signal-dependent autoregressive filters to accurately and efficiently model signal nonlinearities and media noise characteristics, such as signal-dependence and correlation, which are common in magnetic recording signals. This paper evaluates the model's performance using 42 sets of spin-stand data that cover a large range of head/media manufacturers and operating points.

Section: Introductionmentioning

confidence: 97%

Performance of a signal-dependent autoregressive channel model

Stander

Patapoutian²

2000

Statistical study of zig-zag transition boundaries in longitudinal digital magnetic recording

Kavcic

Moura

1997

Self Cite

Abstract-In this paper, we study the statistics of zig-zag transition walls in digital magnetic recording and their relationship to transition noise defining quantities. We provide analytic results that link the statistics of zig-zag transitions to media/recording parameters. The basis of our study is the triangle zig-zag transition (TZ-ZT) model due to its well-defined triangle zig-zag shape and its cross-track stability. The results we derive here, however, are of a general nature, and given the right interpretation, apply to other zig-zag models as well, as we show in the paper. We also provide an interpretation of the cross-track correlation width, linking this quantity to the statistics of magnetized clusters in thin-film magnetic media. The paper concludes by showing how these results can be used in media noise modeling.

Correlation-sensitive adaptive sequence detection

Kavcic

Moura

1998

Self Cite

Abstract-In high density magnetic recording, noise samples corresponding to adjacent signal samples are heavily correlated as a result of front-end equalizers, media noise, and signal nonlinearities combined with nonlinear filters to cancel them. This correlation significantly deteriorates the performance of detectors at high densities. In this paper, we propose a novel sequence detector that is correlation sensitive and adaptive to the nonstationary signal sample statistics. We derive the correlationsensitive maximum likelihood detector. It can be used with any Viterbi-like receiver (e.g., partial response maximum likelihood, fixed delay tree search, multilevel decision feedback equalization) that relies on a tree/trellis structure. Our detector adjusts the metric computation to the noise correlation statistics. Because these statistics are nonstationary, we develop an adaptive algorithm that tracks the data correlation matrices. Simulation results are presented that show the applicability of the new correlationsensitive adaptive sequence detector.