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    A new discrete wavelet transform-assisted convolutional neural network (DWTCNN) equalizer effectively compensates for visible light communication (VLC) system damage. This method significantly reduces nonlinear impairment, improving system performance and bit error rate (BER).

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Visible Light Communication (VLC) systems face challenges from linear and nonlinear signal damage.
    • Existing equalization methods struggle to address diverse damage types comprehensively.
    • Advanced signal processing and deep learning are needed for robust VLC performance.

    Purpose of the Study:

    • To propose a novel equalizer for visible light communication (VLC) systems that addresses both linear and nonlinear damage.
    • To combine the strengths of discrete wavelet transform (DWT) and convolutional neural networks (CNN) for enhanced signal compensation.
    • To improve the bit error rate (BER) and overall system performance in VLC.

    Main Methods:

    • A discrete wavelet transform-assisted convolutional neural network (DWTCNN) equalizer was developed.
    • Wavelet transform decomposes signals into coefficient series, followed by adaptive soft-thresholding to remove redundant information.
    • Reconstructed coefficients achieve complete signal compensation, mitigating nonlinear impairments.

    Main Results:

    • The DWTCNN equalizer significantly reduced nonlinear impairment in VLC systems.
    • Achieved a bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8 × 10-3.
    • Outperformed Long Short-Term Memory (LSTM) and entity extraction neural network (EXNN) equalizers, improving Q factor by 0.76 dB and 0.53 dB, respectively, and increasing DC bias operating ranges by 4.76% and 23.5%.

    Conclusions:

    • The proposed DWTCNN equalizer offers a superior solution for compensating signal damage in VLC systems.
    • This hybrid approach effectively mitigates nonlinear impairments, enhancing system reliability and performance.
    • DWTCNN demonstrates significant advantages over existing deep learning equalization techniques for VLC applications.