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    This study introduces an adaptive deep learning equalizer for underwater visible light communication (UVLC). The novel method reduces computational load while maintaining high-speed data transmission performance.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Visible light communication (VLC) is a key technology for high-speed underwater data transmission.
    • Deep learning equalization improves accuracy but demands significant computational resources.
    • Underwater environments introduce linear and nonlinear impairments affecting signal quality.

    Purpose of the Study:

    • To develop an efficient deep learning equalizer for underwater VLC (UVLC) systems.
    • To reduce the computational complexity of deep learning equalizers without sacrificing performance.
    • To enable real-time data processing in high-speed UVLC systems.

    Main Methods:

    • Proposing an adaptive deep-learning equalizer utilizing a complex-valued neural network.
    • Implementing a constellation partitioning scheme to tailor equalization networks to specific symbol regions.
    • Applying the equalizer to a 64 QAM-CAP modulated UVLC system.

    Main Results:

    • The adaptive partitioned equalizer achieves a bit error rate below the 3.8 × 10-3 limit at 2.85 Gbps.
    • Maintains performance comparable to standard complex-valued networks.
    • Achieves a 56.1% reduction in total computational complexity.

    Conclusions:

    • The proposed adaptive equalizer effectively reduces computational complexity for UVLC systems.
    • This approach facilitates online data processing in high-speed underwater visible light communication.
    • It offers a practical solution for enhancing the efficiency of UVLC systems.