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    A new clustering method significantly improves optical camera communication (OCC) performance. This approach enhances data detection, outperforming traditional methods for future wireless systems.

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    Area of Science:

    • Optoelectronics
    • Wireless Communication Systems
    • Signal Processing

    Background:

    • Optical Camera Communication (OCC) is an emerging wireless technology.
    • Existing OCC systems face performance limitations with traditional linear methods.
    • Temperature-induced spectral variations in LEDs impact OCC system reliability.

    Purpose of the Study:

    • To enhance the performance of Optical Camera Communication (OCC) systems.
    • To introduce and evaluate a novel cluster-based data detection procedure.
    • To compare the proposed method against traditional linear equalizers.

    Main Methods:

    • Utilized a multispectral camera to capture LED spectral variations due to temperature.
    • Implemented a cluster-based data detection procedure for OCC.
    • Compared performance against Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) equalizers.

    Main Results:

    • The cluster-based approach demonstrated improved Bit Error Rate (BER) performance.
    • Significant enhancement in data detection accuracy was observed.
    • The proposed method showed superiority over traditional ZF and MMSE techniques.

    Conclusions:

    • Clustering-based data detection is an effective strategy for enhancing OCC systems.
    • This method offers a promising solution for reliable future wireless communication.
    • Addressing spectral variations through clustering improves overall system robustness.