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    A novel Butterfly Neural Equalizer (NE-Butterfly) effectively addresses nonlinear systems in optical communications. This Artificial Neural Network (ANN) architecture demonstrates superior performance in equalizing complex fiber optic channels.

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    Area of Science:

    • Optical Communications
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Nonlinear systems in optical communications pose significant equalization challenges.
    • Existing neural equalizers struggle with complex channel characteristics.

    Purpose of the Study:

    • Introduce and evaluate a new equalizer architecture, the Butterfly Neural Equalizer (NE-Butterfly).
    • Assess the NE-Butterfly's capability to equalize linear and nonlinear channels with real or complex taps.

    Main Methods:

    • Developed a novel equalizer architecture inspired by the butterfly equalizer.
    • Utilized Multi-Layer Perceptron (MLP) type Artificial Neural Networks (ANNs).
    • Conducted simulations on nonlinear fiber optic channels with inter-symbol interference and additive noise.

    Main Results:

    • The NE-Butterfly successfully equalized diverse nonlinear fiber optic channels.
    • Demonstrated robust performance against channels with complex and real taps.
    • Achieved superior performance compared to existing neural equalizers in the literature.

    Conclusions:

    • The NE-Butterfly offers a highly effective solution for nonlinear channel equalization in optical systems.
    • This architecture presents a significant advancement in neural equalization techniques.
    • The NE-Butterfly validates its superior performance through comparative analysis.