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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Semantic-empowered visible light communication for image transmission based on deep convolutional generative

Wenbin Chen, Tianzheng Ren, Tianxing Yuan

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    This summary is machine-generated.

    This study introduces a semantic communication scheme for visible light communication (VLC) systems, enhancing image transmission efficiency and link stability. The novel approach significantly reduces communication overhead and expands coverage without hardware changes.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Indoor visible light communication (VLC) systems face challenges in transmission efficiency and link stability for image transmission.
    • Conventional methods often require significant overhead for error correction, limiting performance.
    • Deep convolutional generative adversarial networks (DCGAN) offer potential for intelligent data extraction.

    Purpose of the Study:

    • To propose and evaluate a semantic communication-based scheme for optimizing indoor VLC systems.
    • To enhance transmission efficiency and link stability in image transmission over VLC.
    • To reduce communication overhead and expand coverage without hardware modifications.

    Main Methods:

    • Developed a semantic model using DCGAN and vector quantization for essential image feature extraction.
    • Incorporated line-of-sight (LOS) and non-line-of-sight (NLOS) indoor VLC channel models with noise interference.
    • Assessed performance against conventional VLC systems using Better Portable Graphics (BPG) and convolutional codes.
    • Experimentally validated the system on an FPGA-based indoor testbed with up to 1 Gbps data rate.

    Main Results:

    • The semantic VLC system demonstrated a signal-to-noise ratio (SNR) gain exceeding 6 dB.
    • Achieved superior performance over conventional systems without additional bits for error correction.
    • Reduced communication overhead by 40% while maintaining image quality.
    • Doubled communication coverage and ensured stable high-speed image transmission.

    Conclusions:

    • The proposed semantic communication scheme significantly improves efficiency and stability in indoor VLC systems.
    • It offers a practical and effective solution for high-speed image transmission with reduced overhead.
    • The system demonstrates a substantial advancement in VLC technology, expanding its applicability.