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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Pre-equalization scheme for visible light communications with trial-and-error learning.

Shupeng Li, Yi Zou, Fangming Liu

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    Summary
    This summary is machine-generated.

    We developed a new neural network pre-equalizer using trial-and-error learning for visible light communication. This method improves signal quality and noise resilience without needing an extra post-equalizer.

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    Area of Science:

    • Optical Communications
    • Machine Learning
    • Signal Processing

    Background:

    • Visible light communication (VLC) systems face challenges with signal distortion and noise.
    • Existing pre-equalization methods often require complex architectures like indirect learning (IL) with auxiliary post-equalizers.

    Purpose of the Study:

    • To introduce a novel neural network pre-equalizer utilizing a trial-and-error (TE) mechanism for VLC.
    • To eliminate the need for an auxiliary post-equalizer in the transmitter-side training process.

    Main Methods:

    • A TE-based recurrent neural network (RNN) pre-equalizer was designed and implemented.
    • The pre-equalizer was trained directly on the transmitter side through system interaction.
    • Performance was evaluated on a 1.95-Gbps 64-QAM carrier-less amplitude phase (CAP) free-space optical transmission platform.

    Main Results:

    • The TE-RNN pre-equalizer demonstrated superior nonlinear approximation and noise resilience.
    • It achieved signal-to-noise ratio (SNR) gains of 0.8 dB over IL-RNN and 1.8 dB over IL-Volterra pre-equalizers.
    • This represents the first application of TE learning for pre-equalizer training in VLC.

    Conclusions:

    • The proposed TE-based neural network pre-equalizer offers an effective and simpler solution for VLC systems.
    • Direct transmitter-side training enhances system performance and robustness.
    • This novel approach advances the field of optical communication equalization.