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    Machine learning corrects distorted optical modes in free-space optical links. This artificial neural network approach uses intensity profiles to ensure accurate signal transmission, enhancing communication robustness.

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    Area of Science:

    • Optics
    • Machine Learning
    • Optical Communications

    Background:

    • Turbulent propagation significantly distorts optical modes in free-space optical (FSO) links.
    • Maintaining signal integrity and mode quality is crucial for reliable FSO communication.

    Purpose of the Study:

    • To develop and demonstrate a machine learning-based optical feedback network for correcting turbulence-induced distortions.
    • To enhance the robustness and reliability of free-space optical links.

    Main Methods:

    • Designing an optical feedback network utilizing artificial neural networks (ANNs).
    • Employing intensity profile measurements of distorted optical modes as input for the ANN.
    • Simulating the network's performance in correcting mode distortions caused by atmospheric turbulence.

    Main Results:

    • The ANN successfully generated transmitter mode profiles that, after turbulent propagation, closely matched desired target modes.
    • Corrected optical mode profiles at the receiver showed near-zero mean square error compared to target profiles.
    • The proposed method demonstrated simplicity and robustness by relying solely on intensity profile measurements.

    Conclusions:

    • The integration of machine learning with optical communications offers a powerful solution for mitigating turbulence effects.
    • This approach significantly enhances the robustness of free-space optical links, paving the way for more reliable FSO systems.