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Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
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Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects.

Ji Zhou, Haide Wang, Jinlong Wei

    Optics Express
    |November 6, 2019
    PubMed
    Summary

    The Adaptive Moment Estimation (Adam) algorithm offers faster, more stable convergence for polynomial nonlinear equalizer tap coefficients in high-speed optical interconnects. It outperforms traditional methods by avoiding local optima and resisting timing errors.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning Optimization

    Background:

    • Polynomial Nonlinear Equalizers (PNLE) are crucial for managing signal distortions in high-speed optical interconnects.
    • Traditional adaptive algorithms like Least-Mean Square (LMS) can struggle with convergence speed and local optima in complex systems.
    • High-baud rate and high-order modulation schemes, such as 129-Gbit/s PAM8, exacerbate challenges like timing errors.

    Purpose of the Study:

    • To introduce and evaluate the Adaptive Moment Estimation (Adam) algorithm for optimizing polynomial nonlinear equalizer (PNLE) tap coefficients.
    • To demonstrate the efficacy of Adam in achieving fast and stable convergence for large-scale tap coefficients in optical interconnects.
    • To compare Adam's performance against traditional adaptive algorithms, specifically in the context of 129-Gbit/s PAM8 systems.

    Main Methods:

    • Application of the Adam optimization algorithm to estimate large-scale tap coefficients of a PNLE.
    • Implementation of PNLE within a 129-Gbit/s PAM8-based optical interconnect system.
    • Comparative analysis of Adam against serial LMS adaptive algorithms, focusing on convergence speed, stability, and Mean Squared Error (MSE).

    Main Results:

    • Adam algorithm achieves fast and stable convergence of PNLE tap coefficients, outperforming the LMS algorithm.
    • Adam demonstrates superior performance in resisting timing errors, a critical issue in high-baud rate PAM systems.
    • The Adam algorithm achieves lower MSE and more robust convergence compared to the LMS adaptive algorithm.

    Conclusions:

    • The Adam algorithm presents a significant advancement for optimizing PNLE in high-performance optical interconnects.
    • Adam's parallel processing and global optimization capabilities enable faster, more stable, and accurate tap coefficient convergence.
    • Adam shows substantial potential for future applications in advanced optical communication systems requiring efficient nonlinear equalization.