Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks

Area of Science:

• Optical Engineering
• Computational Electromagnetics
• Telecommunications

Background:

• Distributed Raman amplifiers (DRAs) are crucial for optical signal amplification over long distances.
• Accurate design of DRAs requires precise control of pump powers and wavelengths to achieve a target signal power profile.
• Traditional design methods can be complex and computationally intensive.

Purpose of the Study:

• To develop a novel convolutional neural network (CNN) architecture for the inverse design of Raman amplifiers.
• To predict the required pump powers and wavelengths for a specific signal power evolution along the fiber and across frequencies.
• To demonstrate the framework's accuracy in designing DRAs for C-band applications.

Main Methods:

• Implementation of a CNN architecture for inverse Raman amplifier design.
• Numerical simulation of counter-propagating and bidirectional pumping schemes.
• Validation of the CNN's prediction accuracy for pump configuration against target signal power profiles.
• Analysis of prediction errors (mean and standard deviation) for different pump configurations.

Main Results:

• The CNN accurately predicts pump configurations for target signal power evolution in DRAs.
• High accuracy was achieved for both counter-propagating and bidirectional pumping schemes in the C-band.
• Low mean and standard deviation of maximum test errors were obtained for 2, 3, and 4 pump configurations.
• The model demonstrated effectiveness for a 100 km single-mode fiber DRA.

Conclusions:

• The proposed CNN framework offers an efficient and accurate method for inverse Raman amplifier design.
• This AI-driven approach simplifies the complex task of optimizing pump parameters for DRAs.
• The findings have significant implications for advancing optical fiber amplifier design and performance.