In this paper we present a comparative analysis of possible simplifications of various nonlinear receivers for the high density optical disc. Specifically, Nonlinear Adaptive Volterra Equalization (NAVE), Nonlinear Adaptive Decision Feedback Equalization (NDFE), and Nonlinear Maximum Likelihood Sequence Estimation (NMLSE) are evaluated and compared. Reduced complexity NAVE and NDFE are obtained including fewer linear and nonlinear taps than required by full implementations. The NMLSE structure can be simplified not only reducing the number of (linear and nonlinear) taps of the preliminary equalizer, but also the number of trellis states and the path truncation length (i.e., memory) of the Viterbi detector. The performance comparison presented in this paper is based on a nonlinear channel model represented by a Volterra series. For the sake of simplicity, we assume that noise is additive, white, and Gaussian (AWGN model). Simulation results indicate that considerable simplifications are possible, at the expense of small performance degradations.
Simplified nonlinear receivers for the optical disc channel
MIGLIORATI, Pierangelo
1999-01-01
Abstract
In this paper we present a comparative analysis of possible simplifications of various nonlinear receivers for the high density optical disc. Specifically, Nonlinear Adaptive Volterra Equalization (NAVE), Nonlinear Adaptive Decision Feedback Equalization (NDFE), and Nonlinear Maximum Likelihood Sequence Estimation (NMLSE) are evaluated and compared. Reduced complexity NAVE and NDFE are obtained including fewer linear and nonlinear taps than required by full implementations. The NMLSE structure can be simplified not only reducing the number of (linear and nonlinear) taps of the preliminary equalizer, but also the number of trellis states and the path truncation length (i.e., memory) of the Viterbi detector. The performance comparison presented in this paper is based on a nonlinear channel model represented by a Volterra series. For the sake of simplicity, we assume that noise is additive, white, and Gaussian (AWGN model). Simulation results indicate that considerable simplifications are possible, at the expense of small performance degradations.File | Dimensione | Formato | |
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