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    Area of Science:

    • Electrical Engineering
    • Computer Science
    • Signal Processing

    Background:

    • Rolling shutter cameras in optical camera communication (OCC) face bandwidth limitations due to exposure time.
    • Long exposures cause inter-symbol interference (ISI) by averaging light, creating spatial mixtures in images.
    • Short exposure times yield dark images unsuitable for practical applications.

    Purpose of the Study:

    • To propose a novel equalizer to mitigate exposure-related ISI and noise in OCC systems.
    • To develop a training method that enhances network fitting and decoding robustness across various conditions.
    • To improve reception bandwidth and bit error rates (BER) in challenging optical communication scenarios.

    Main Methods:

    • A convolutional autoencoder-based equalizer was designed for ISI and noise mitigation.
    • Offline training was performed using synthetic images, independent of specific deployment scenarios.
    • The system was evaluated for Manchester encoded on-off keying signals.

    Main Results:

    • The equalizer significantly mitigated ISI for exposure times up to seven times the symbol duration.
    • Achieved bit error rates (BER) below 10-5 under optimal signal-to-noise ratio (SNR) conditions.
    • Reception bandwidth improved up to 14 times compared to non-equalized systems.
    • Under harsh SNR conditions, BERs were below forward error correction limits with extended exposure times.

    Conclusions:

    • The proposed equalizer effectively addresses exposure-related limitations in rolling shutter OCC.
    • Offline training with synthetic data enhances system robustness and applicability across diverse camera and SNR conditions.
    • Significant improvements in reception bandwidth and error rates were demonstrated, enabling practical OCC applications.