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High noise margin decoding of holographic data page based on compressed sensing.

Jinpeng Liu, Le Zhang, Anan Wu

    Optics Express
    |April 1, 2020
    PubMed
    Summary

    A novel decoding method using compressed sensing significantly improves holographic data storage by reducing noise impact. This method enhances data reconstruction quality, outperforming traditional techniques in simulations and experiments.

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    Area of Science:

    • Optics and Data Storage
    • Signal Processing

    Background:

    • Holographic data storage systems rely on high-quality reconstructed data patterns for optimal performance.
    • Noise from optical, electronic, and material components degrades reconstruction quality, impacting system efficiency.
    • Conventional threshold decoding methods are susceptible to noise, limiting their effectiveness in multilevel modulation schemes.

    Purpose of the Study:

    • To introduce a high noise margin decoding method based on compressed sensing technology.
    • To mitigate the detrimental effects of noise on data reconstruction in holographic storage.
    • To evaluate the performance of the proposed decoding method, particularly for multilevel modulation.

    Main Methods:

    • Development of a novel decoding algorithm leveraging compressed sensing principles.
    • Implementation and evaluation of the proposed method using five-level amplitude modulation.
    • Comparative analysis against conventional threshold decoding through both simulation and experimental validation.

    Main Results:

    • The compressed sensing-based decoding method demonstrated superior robustness against combined Gaussian, Rician, and Rayleigh noise.
    • Simulations showed a reduction in bit error rate (BER) to one-sixth compared to the threshold method at an SNR of -1.
    • Experimental results indicated the proposed method performed up to 8.3 times better than conventional threshold decoding.

    Conclusions:

    • The high noise margin decoding method effectively combats noise in holographic data storage.
    • This compressed sensing-derived approach offers significant advantages over traditional decoding techniques, especially for multilevel data.
    • The method shows promise for enhancing the reliability and performance of future holographic data storage systems.