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An adaptive all-pass filter for decision feedback equalization



Graduation date: 1997Increasing densities on magnetic data storage devices leads to problems of\ud severe intersymbol interference (ISI), additive noise and non-linearities. Advanced\ud detection strategies for magnetic recording channels fall into two categories: partial\ud response equalization with maximum likelihood decoding and decision feedback\ud equalization. This study focuses on doing an adaptive all-pass forward filter for\ud the decision feedback channel. The decision feedback channel can be equalized by\ud a low-order continuous-time filter, and does not require a transversal filter with\ud high-precision multiplication. This results in considerable savings in both power\ud consumption and chip die area. One problem that has yet to be addressed is how to\ud adaptively set the coefficients of the all-pass filter. This thesis examines the design\ud and performance of an adaptive all-pass filter.\ud The performances in terms of the mean-squared error (MSE) of a first- and\ud second-order all-pass are evaluated. They are compared to a conventional FIR filter\ud design of various lengths. An adaptive algorithm based on the least mean-squared\ud (LMS) error is developed and characterized over a range of storage densities. Since\ud this does not require sampling of the filter input or any states of the forward filter, the system could be realized in continuous-time up to the decision device.\ud Numerical simulations for various data densities and noise variances are done to verify the theoretically expected performance and the adaptation behavior of the all-pass

Year: 1997
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Provided by: ScholarsArchive@OSU

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