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Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
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Simulation and Modelling of C+L+S Multiband Optical Transmission for the OCATA Time Domain Digital Twin.

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Machine learning models predict optical signal propagation for faster quality of transmission estimation in C+L+S multiband optical networks. This enables efficient network planning and operation, addressing future traffic demands.

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Area of Science:

  • Optical Communications
  • Network Engineering
  • Machine Learning

Background:

  • C+L+S multiband (MB) optical transmission is crucial for increasing optical transport network capacity to meet rising traffic demands.
  • Accurate quality of transmission (QoT) estimation tools are essential for planning and operating MB optical networks.
  • Existing methods like split-step Fourier method (SSFM) are computationally intensive for real-time QoT estimation.

Purpose of the Study:

  • To develop fast and accurate machine learning (ML) models for QoT estimation in MB optical transmission.
  • To integrate these ML models into an optical layer digital twin (DT) solution for network automation.
  • To compare different ML approaches for predicting optical signal propagation and QoT accuracy.

Main Methods:

  • Utilized the fourth-order Runge-Kutta in the interaction picture (RK4IP) method with adaptive step size for efficient signal propagation modeling.
  • Generated datasets using RK4IP for training neural network-based ML models.
  • Developed and compared two ML modeling approaches for time-domain optical signal prediction within a digital twin.

Main Results:

  • The RK4IP method achieved accuracy comparable to SSFM but with significantly reduced computation time, enabling MB optical transmission simulation.
  • The developed ML models accurately predict optical signal propagation in the time domain.
  • The study focused on comparing the general and QoT estimation accuracy of the proposed ML approaches.

Conclusions:

  • Fast and accurate QoT estimation for MB optical networks is achievable using ML models integrated into a digital twin.
  • The RK4IP method offers an efficient alternative for simulating complex MB optical transmission effects like interchannel stimulated Raman scattering (ISRS).
  • ML-based signal prediction facilitates automated network operations, including connection provisioning and failure management.