In this paper, we show that by combining experimental data from different optical fibers, we
can build a fiber-agnostic neural-network to model the Raman amplifier. The fiber-agnostic …

Machine learning-based Raman amplifier design Page 1 M1J.1.pdf OFC 2019 © OSA 2019
Machine learning-based Raman amplifier design D. Zibar1, A. Ferrari2, V. Curri2 and A …

A machine learning framework predicting pump powers and noise figure profile for a target
distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single …

An ultra-fast machine learning based method for accurate predictions of gain and amplified
spontaneous emission (ASE) noise profiles of Raman amplifiers is introduced. It is an …

In this paper, a novel robust, compact, and flexible neural-network model for a fiber Raman
amplifier (FRA) is presented. The model can be used in various applications with promising …

One of the most promising solutions to overcome the capacity limit of current optical fiber
links is space-division multiplexing, which allows the transmission on various cores of multi …

We present a convolutional neural network architecture for inverse Raman amplifier design.
This model aims at finding the pump powers and wavelengths required for a target signal …

Optical communication systems are always evolving to support the need for ever–increasing
transmission rates. This demand is supported by the growth in complexity of communication …

We address the different design challenges and applications of machine learning to
modeling optical amplifiers. The problem of accuracy in designing neural networks cannot …

A machine learning method for Raman gain prediction and multi-pump broadband amplifier
design is experimentally demonstrated over a 100 nm-wide optical bandwidth. We show …