Spectral and spatial power evolution design with machine learning-enabled Raman amplification

TL;DR

This paper introduces a machine learning-based framework combining CNN and differential evolution to precisely design spectral and spatial power profiles in Raman amplification for fiber optic systems.

Contribution

It presents a novel ML framework that improves the accuracy of designing 2D power profiles in Raman amplification by combining CNN predictions with DE fine-tuning.

Findings

Achieved a maximum power excursion of 2.81 dB for flat profiles.

Attained a maximum asymmetry of 14% for symmetric profiles.

Demonstrated effectiveness over the entire C-band for WDM systems.

Abstract

We present a machine learning (ML) framework for designing desired signal power profiles over the spectral and spatial domains in the fiber span. The proposed framework adjusts the Raman pump power values to obtain the desired two-dimensional (2D) profiles using a convolutional neural network (CNN) followed by the differential evolution (DE) technique. The CNN learns the mapping between the 2D profiles and their corresponding pump power values using a data-set generated by exciting the amplification setup. Nonetheless, its performance is not accurate for designing 2D profiles of practical interest, such as a 2D flat or a 2D symmetric (with respect to the middle point in distance). To adjust the pump power values more accurately, the DE fine-tunes the power values initialized by the CNN to design the proposed 2D profile with a lower cost value. In the fine-tuning process, the DE employs the direct amplification model which consists of 8 bidirectional propagating pumps, including 2 second-order and 6 first order, in an 80 km fiber span. We evaluate the framework to design broadband 2D flat and symmetric power profiles, as two goals for wavelength division multiplexing (WDM) system performing over the whole C-band. Results indicate the framework's ability to achieve maximum power excursion of 2.81 dB for a 2D flat, and maximum asymmetry of 14% for a 2D symmetric profile.