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    Area of Science:

    • Optoelectronics and Optical Communications
    • Artificial Intelligence in Communications
    • Wireless Networking Technologies

    Background:

    • Visible Light Communication (VLC) is a key technology for future 6G networks, offering high bandwidth and security.
    • Accurate monitoring of VLC impairments like turbulence is crucial for network adaptability but remains underdeveloped.
    • Existing methods for signal-to-noise ratio (SNR) estimation in VLC are limited in performance and scope.

    Purpose of the Study:

    • To experimentally demonstrate a novel deep-neural-network-based SNR estimation scheme for VLC networks.
    • To compare the performance of a Vision Transformer (ViT) model against a Convolutional Neural Network (CNN) for SNR estimation.
    • To investigate the impact of data augmentation on improving SNR estimation accuracy in VLC.

    Main Methods:

    • Implementation of a Vision Transformer (ViT) model for SNR estimation in VLC systems.
    • Comparison of ViT performance against a traditional Convolutional Neural Network (CNN) based scheme.
    • Utilization of a contour stellar image dataset with varying SNR levels and application of data augmentation techniques.

    Main Results:

    • The ViT-based scheme demonstrated superior SNR estimation accuracy compared to the CNN-based scheme across various Quadrature Amplitude Modulation (QAM) levels.
    • ViT achieved accuracies of 76% (2QAM), 63.33% (4QAM), 45.33% (8QAM), and 37.67% (16QAM).
    • Data augmentation further enhanced ViT's accuracy to 95% (2QAM), 79.67% (4QAM), 58.33% (8QAM), and 50.33% (16QAM).

    Conclusions:

    • The ViT model offers a robust and accurate solution for real-time SNR estimation in VLC networks.
    • Data augmentation is a critical factor in maximizing the performance of deep learning models for VLC impairment monitoring.
    • This work lays the foundation for adaptive and reliable VLC network management in future communication systems.