Effective Nonlinear Receivers for High Density Optical Recording

Luigi AGAROSSI  Sandro BELLINI  Pierangelo MIGLIORATI  

Publication
IEICE TRANSACTIONS on Electronics   Vol.E85-C   No.9   pp.1675-1683
Publication Date: 2002/09/01
Online ISSN: 
DOI: 
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Optoelectronics
Keyword: 
nonlinear equalization,  optical recording,  Volterra models,  compact disc,  cross talk,  

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Summary: 
The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).