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Deep learning-enhanced NLOS NOMA-VLC system based on chirp spread spectrum modulation.

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    This summary is machine-generated.

    This study introduces a novel non-line-of-sight (NLOS) visible light communication (VLC) system using chirp spread spectrum (CSS) and a denoising-attention-graph neural network (DAG-SICNet) for enhanced wireless connectivity.

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    Area of Science:

    • Wireless communication technologies
    • Optical wireless communication systems
    • Smart city infrastructure

    Background:

    • Visible Light Communication (VLC) is a key technology for smart cities, complementing radio frequency (RF) networks.
    • Non-orthogonal Multiple Access (NOMA) enhances spectral efficiency in VLC.
    • Non-line-of-sight (NLOS) VLC faces challenges like signal attenuation and multipath effects, hindering reliability.

    Purpose of the Study:

    • To propose a robust NLOS NOMA-VLC system using Chirp Spread Spectrum (CSS) modulation.
    • To develop a novel demodulator, DAG-SICNet, for signal compensation and recovery in mobile VLC environments.
    • To address challenges in obtaining accurate channel state information for mobile VLC.

    Main Methods:

    • Implementation of an NLOS NOMA-VLC system with CSS modulation.
    • Development of a denoising-attention-graph neural network combined with successive interference cancellation (DAG-SICNet) based demodulator.
    • Joint signal compensation and recovery using the proposed DAG-SICNet architecture.

    Main Results:

    • The DAG-SICNet demodulator effectively mitigates linear and nonlinear impairments from NLOS propagation.
    • Interference between multiple users in the NOMA-VLC system is successfully eliminated.
    • Achieved data rates of 40.7 Mbps (User1) and 24.3 Mbps (User2) over 1.5 m, demonstrating high reliability.

    Conclusions:

    • The proposed NLOS NOMA-VLC system with CSS modulation and DAG-SICNet offers a reliable solution for obstructed wireless links.
    • The DAG-SICNet demodulator significantly improves signal quality and user data rates in challenging VLC environments.
    • This approach enhances the robustness and performance of VLC for future smart city applications.