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Nonlinear SNR estimation based on the data augmentation-assisted DNN with a small-scale dataset.

Weiwei Zhao, Yijun Cheng, Meng Xiang

    Optics Express
    |October 27, 2022
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    Data augmentation assists deep neural networks for accurate nonlinear signal-to-noise ratio estimation in optical systems. This method significantly reduces dataset size while maintaining high performance, crucial for practical applications.

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    Area of Science:

    • Optical Communications
    • Signal Processing
    • Machine Learning

    Background:

    • Fiber nonlinearity is a key challenge in long-haul optical transmission systems.
    • Accurate estimation of nonlinear signal-to-noise ratio (SNRNL) is vital for transmission quality and network management.
    • Deep neural networks (DNNs) show promise for SNRNL estimation but require large datasets.

    Purpose of the Study:

    • To develop an accurate SNRNL estimation method using a data augmentation-assisted DNN (DA-DNN) with small datasets.
    • To evaluate the performance of the DA-DNN in the frequency domain for optical transmission systems.

    Main Methods:

    • Numerical transmission of 95-GBaud DP-16QAM signals through standard single-mode fiber (SSMF) from 80-km to 1520-km.
    • Application of data augmentation (DA) techniques to a DNN for SNRNL estimation.
    • Comparison of the DA-DNN with a traditional DNN scheme using mean absolute error (MAE).

    Main Results:

    • The DA-DNN reduced the required training dataset size by approximately 60% compared to the traditional DNN.
    • The DA-DNN achieved the same MAE of 0.2-dB for SNRNL estimation as the traditional DNN.
    • The DA-DNN scheme improved MAE by about 0.14-dB compared to the traditional DNN when using limited raw datasets.

    Conclusions:

    • DA-assisted DNN provides accurate SNRNL estimation even with small datasets.
    • This approach is highly promising for real-world field trials where large dataset collection is challenging.
    • The DA-DNN offers a practical solution for managing optical network operations and ensuring transmission quality.