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    Area of Science:

    • Optical Communications
    • Wavefront Engineering
    • Machine Learning

    Background:

    • Free-space optical communication utilizes orbital angular momentum (OAM) for high channel capacity.
    • Atmospheric turbulence degrades the modal orthogonality essential for conventional OAM receivers.
    • Existing methods struggle with maintaining signal integrity under turbulent atmospheric conditions.

    Purpose of the Study:

    • To develop a turbulence-resilient method for orbital angular momentum (OAM) classification in free-space optical communication.
    • To demonstrate the efficacy of a passive multimode fiber reservoir acting as an optical extreme learning machine.
    • To outperform traditional modal decomposition techniques in moderate-to-strong atmospheric turbulence.

    Main Methods:

    • Employing a passive multimode fiber reservoir as an optical extreme learning machine.
    • Mapping distorted OAM wavefronts into a high-dimensional speckle feature space.
    • Utilizing linear readouts for OAM classification after wavefront distortion.

    Main Results:

    • Achieved high accuracy in classifying structural (MNIST, 97.6%) and textural (Fashion-MNIST, 87.1%) benchmarks.
    • Demonstrated superior performance over ideal modal decomposition in moderate-to-strong turbulence (exceeding 20% in strong turbulence regimes, SR ≤ 0.5).
    • Showcased the system's ability to maintain OAM class separability in a high-dimensional feature space.

    Conclusions:

    • Passive optical scattering in a fiber reservoir effectively processes complex wavefronts for robust OAM recovery.
    • The optical extreme learning machine approach offers a significant advancement for free-space optical communication under atmospheric turbulence.
    • This all-optical method enables simple linear readouts for reliable OAM classification, even when modal orthogonality is compromised.