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Proceedings Paper

Recurrent neural network equalization for partial response shaping of magneto-optical readback signals
Author(s): Inci Ozgunes; Kadri Hacioglu; Bhagavatula Vijaya Kumar
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Paper Abstract

In this paper, use of recurrent neural network equalizer (RNNE) in place of linear equalizer (LE) to combat both linear and nonlinear distortions corrupting the Magneto-optical (MO) readback signal is discussed. It is shown that RNNE can outperform LE without introducing significant complexity. RNNE is used to equalize the MO recording readback signal corrupted by transition jitter, intersymbol interference (ISI) and additive white Gaussian Noise (AWGN) at a density of 50 kbpi. The MO signal is equalized to a partial response (PR) (1 + D) using either the RNNE or the LE and the equalizer's mean- squared-error (MSE) performance is compared. Then, the equalized signal is passed through a detector and it is shown that a signal equalized to a PR (1 + D) shape can be detected using either a bit-by-bit type of detector (BD) or a sequence detector implemented via Viterbi Algorithm (VA). The bit-error-rate (BER) performance of BD is compared to that of the Viterbi detector and it is shown that PR equalization of MO readback signals using RNNE improves MSE performance over linear equalizer, allowing use of BD rather than LE + Viterbi Algorithm with comparable BERs.

Paper Details

Date Published: 23 October 1998
PDF: 9 pages
Proc. SPIE 3401, Optical Data Storage '98, (23 October 1998); doi: 10.1117/12.327941
Show Author Affiliations
Inci Ozgunes, Eastern Mediterranean Univ. (United States)
Kadri Hacioglu, Eastern Mediterranean Univ. (Turkey)
Bhagavatula Vijaya Kumar, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 3401:
Optical Data Storage '98
Shigeo R. Kubota; Tomas D. Milster; Paul J. Wehrenberg, Editor(s)

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