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Optimal precompensation for nonlinearities in longitudinal magnetic recording using dynamic programming

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  • Fabian Lim
  • Aleksandar Kavcic

Abstract

We propose a dynamic programming approach to compute optimal precompensation that minimises the Mean Squared Error (MSE) between a nonlinear read-back signal and a linear one. This is done by assuming that the nonlinear read-back signal can be represented by a known finite state machine model. We show how to use finite and infinite horizon dynamic programming techniques to minimise the MSE cost function. Suboptimal techniques are studied, and performance comparisons are performed using computer simulations.

Suggested Citation

  • Fabian Lim & Aleksandar Kavcic, 2008. "Optimal precompensation for nonlinearities in longitudinal magnetic recording using dynamic programming," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 5(3/4), pages 410-429.
    • Handle: RePEc:ids:ijpdev:v:5:y:2008:i:3/4:p:410-429
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