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Area of Science:

Optical Engineering
Computational Physics
Machine Learning Applications

Background:

Raman amplifiers are crucial for optical communication systems.
Accurate modeling of optical power evolution in fibers is essential.
Existing methods for power prediction are often limited in resolution or computationally intensive.

Purpose of the Study:

To develop a novel deep learning approach for predicting distributed optical power evolution in wideband discrete Raman amplifiers.
To achieve high spatial resolution (1 km) power distribution prediction across 40 channels simultaneously.
To demonstrate the integration of a mixture-of-experts (MoE) model with optimization techniques for Raman amplifier design.

Main Methods:

Implemented a deep learning network utilizing a mixture-of-experts (MoE) framework.
Developed a model for signal and pump power evolution within the MATLAB environment.
Combined the MoE model with the Optuna multi-objective optimizer for optimization tasks.

Main Results:

Successfully predicted optical power distribution along the fiber with 1 km spatial resolution for 40 channels.
Achieved a root mean squared error (RMSE) below 0.02 across all channels.
Demonstrated the computational efficiency and applicability of the MoE framework in Raman amplifier optimization.

Conclusions:

The MoE-based deep learning framework provides an accurate and efficient method for modeling distributed optical power in Raman amplifiers.
This approach surpasses traditional methods by offering simultaneous multi-channel, high-resolution power evolution prediction.
The integration with Optuna signifies a novel contribution to Raman amplifier design and optimization.