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2-D quantization scheme utilizing SOFM neural network clustering for a DRoF system.

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    This study introduces a novel two-dimensional (2-D) quantization scheme using self-organizing feature map (SOFM) neural networks for digitized radio-over-fiber (DRoF) systems. This method enhances spectral efficiency and transmission performance compared to traditional approaches.

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    Area of Science:

    • Optical communications
    • Signal processing
    • Artificial intelligence

    Background:

    • Digitized radio-over-fiber (DRoF) systems are crucial for modern wireless communication infrastructure.
    • Conventional scalar quantization methods in DRoF systems face limitations in spectral efficiency and performance.
    • Optimizing quantization is key to improving data transmission in high-speed optical networks.

    Purpose of the Study:

    • To propose and experimentally validate a novel two-dimensional (2-D) quantization scheme for DRoF systems.
    • To improve spectral efficiency and transmission performance in DRoF systems.
    • To compare the proposed 2-D quantization with conventional scalar quantization methods.

    Main Methods:

    • Implementation of a two-dimensional (2-D) quantization scheme using a self-organizing feature map (SOFM) neural network.
    • Construction of sampled sequences as 2-D arrays.
    • Experimental demonstration in a 25 km, 5 Gbaud/λ intensity modulation and direct detection optical link using four-level pulse amplitude modulation (PAM-4).

    Main Results:

    • The SOFM-based 2-D quantization achieved superior error vector magnitude performance compared to scalar quantization for the same number of quantization bits.
    • Successful demonstration of carrier aggregations for 5G New Radio (NR) standards, supporting various orthogonal frequency division multiplexing (OFDM) and quadrature amplitude modulation (QAM) configurations.
    • Achieved 3, 4, 5, and 6 bits per sample for 4, 16, 64, and 256 QAM, respectively.

    Conclusions:

    • The proposed 2-D quantization scheme effectively improves spectral efficiency and transmission performance in DRoF systems.
    • SOFM neural network clustering provides an optimized quantization codebook for enhanced error vector magnitude.
    • The scheme is suitable for advanced 5G NR applications requiring high data rates and spectral efficiency.