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Flexible optical fiber channel modeling based on a neural network module.

Rui Jiang, Zhi Wang, Tao Jia

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    |August 15, 2023
    PubMed
    Summary

    A new neural network module, NNSpan, accurately emulates long-haul optical transmission systems. This AI approach significantly speeds up simulations compared to traditional methods, aiding optical system design.

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    Area of Science:

    • Optical Communications
    • Computational Photonics
    • Artificial Intelligence in Engineering

    Background:

    • Optical fiber channel modeling is crucial for transmission system design but is computationally intensive using the split-step Fourier method (SSFM).
    • Existing SSFM-based simulations require significant time due to iterative steps, limiting rapid system design and analysis.

    Purpose of the Study:

    • To develop a computationally efficient neural network module (NNSpan) for emulating optical fiber channel transfer functions.
    • To validate the accuracy and performance of NNSpan in emulating long-haul optical transmission systems.

    Main Methods:

    • Training a neural network module (NNSpan) to learn the transfer function of 80 km G652 or G655 fiber spans.
    • Cascading multiple trained NNSpans to emulate transmission over 1000 km.
    • Evaluating NNSpan performance with and without erbium-doped fiber amplifier (EDFA) noise, and with optional optical bandpass filters.

    Main Results:

    • NNSpan achieved remarkable prediction accuracy in emulating long-haul optical transmission systems up to 1000 km.
    • The NNSpan model demonstrated robust performance even when simulating systems affected by EDFA noise, despite not being explicitly trained on noise.
    • NNSpan provided a significant computational advantage, reducing computation time by a factor of 12 compared to SSFM.

    Conclusions:

    • NNSpan offers a computationally efficient and accurate alternative for optical transmission system simulations.
    • This AI-driven approach can accelerate optical system design and analysis, serving as a valuable supplementary tool.