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Multidimensional vector quantization-based fast statistical estimation in compressed digitalized radio-over-fiber

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    A new multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm improves data compression for digitalized radio over fiber (D-RoF) systems. This VQ-FSE method offers enhanced compression ratios and lower computational complexity compared to existing techniques.

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    Area of Science:

    • Telecommunications Engineering
    • Signal Processing
    • Data Compression

    Background:

    • Digitalized Radio over Fiber (D-RoF) systems require efficient data compression for performance enhancement.
    • Existing methods like scalar quantization and k-means clustering have limitations in compression ratio or computational complexity.

    Purpose of the Study:

    • To propose a novel multidimensional vector quantization-based fast statistical-estimation (VQ-FSE) algorithm for D-RoF systems.
    • To enhance data compression performance and reduce computational complexity in D-RoF systems.

    Main Methods:

    • Companding transformation to convert Gaussian-distributed samples to uniformly distributed samples.
    • Multidimensional signal vector construction with low inter-sample correlation.
    • Multidimensional uniform quantization applied to transformed signal vectors.

    Main Results:

    • The proposed 2D VQ-FSE algorithm was numerically verified in a 20 km D-RoF system (2 Gbit/s RF signal).
    • Achieved significantly enhanced compression ratio compared to scalar-quantization-based FSE.
    • Demonstrated a similar compression ratio but lower computational complexity than 2D k-means-clustering-based VQ.

    Conclusions:

    • The VQ-FSE algorithm provides superior data compression for D-RoF systems with Gaussian-distributed samples.
    • Offers a favorable balance of high compression ratio and reduced computational load.
    • Presents a viable solution for optimizing performance in digitized D-RoF applications.