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Fast QoT estimation method using cascaded artificial neural network for real-time path provisioning in IMDD based

Ryo Igarashi, Ryo Koma, Kazutaka Hara

    Optics Express
    |February 1, 2024
    PubMed
    Summary

    We developed a fast artificial neural network (ANN) method for quality of transmission (QoT) estimation in intensity modulation-direct detection (IMDD) systems. This approach accurately predicts bit error rates, accounting for chromatic dispersion and self-phase modulation (SPM).

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    Area of Science:

    • Optical communications engineering
    • Artificial intelligence in telecommunications
    • Signal processing for optical networks

    Background:

    • Quality of Transmission (QoT) estimation is crucial for optimizing optical network performance.
    • Traditional methods for QoT estimation can be computationally intensive and slow.
    • Intensity Modulation-Direct Detection (IMDD) systems are widely used in short-reach optical communication.

    Purpose of the Study:

    • To propose a computationally efficient method for QoT estimation in IMDD systems.
    • To accurately predict the bit error rate (BER) considering deterministic waveform distortions.
    • To enable real-time performance monitoring and network management.

    Main Methods:

    • Development of a cascaded Artificial Neural Network (ANN) model.

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  • Incorporation of deterministic waveform distortion models, including chromatic dispersion and self-phase modulation (SPM).
  • Validation of the ANN model for a three-span 36 km transmission link.

    • Main Results:

    • The proposed ANN-based method achieves fast QoT estimation, completing calculations within 0.7 seconds.
    • The method accurately accounts for signal impairments like chromatic dispersion and SPM.
    • Achieved high accuracy in predicting bit error rates for the simulated transmission scenario.

    Conclusions:

    • Cascaded ANNs offer a viable and efficient solution for real-time QoT estimation in IMDD systems.
    • The proposed method enhances the capability for dynamic network management and resource allocation.
    • This approach contributes to the development of more robust and performant optical communication networks.