Optimization of Raman amplifiers using machine learning

It has been recently demonstrated that neural networks can learn the complex pump–signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly–accurate Raman amplifier designs and flexible configuration for ultra–wideband optical communication systems

Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

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-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly accurate gain profile designs and noise figure predictions, with a maximum error on average of ∼ 0.3 dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification

Experimental Characterization of Raman Amplifier Optimization through Inverse System Design

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 systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this article, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5 dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase

Introducing Load Aware Neural Networks for Accurate Predictions of Raman Amplifiers

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 alternative to high-complexity and time-consuming standard approaches, which are based on the numerical solution of sets of nonlinear differential equations. Main relevance resides on its possible application in real-Time network controllers for future multi-band optical line systems where Raman amplification will be required to cope with capacities beyond the standard C-band. Here we consider as an example the C+L-band scenario with different input load conditions: full load and partial loads. For the case of full load it has been recently shown a neural network (NN) capable of highly accurate predictions. Real optical networks are not usually operated only in full load conditions: The load can dynamically vary over time and the behavior of the Raman amplifier depends on it. In this article we introduce a new NN model and we show its higher accuracy when the line system is not fully loaded: we define it as the load aware neural network. Applying this new approach we can predict both gain and ASE noise profiles in Raman amplifiers with high accuracy under any load conditions: we demonstrate almost 100% of maximum prediction errors to be lower than 0.5 dB

Generalization Properties of Machine Learning-based Raman Models

We investigate the generalization capabilities of neural network-based Raman amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber-specific models

Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering

Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in presence of inter-channel stimulated Raman scattering (ISRS) and to support non-linear interference (NLI) modeling. High prediction accuracy is obtained with maximum errors ≤ 0.1 dB over thousands different partial loads

Load aware Raman gain profile prediction in dynamic multi-band optical networks

We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables future network controllers to manage seamless upgrades toward multi-band optical line systems with dynamic loads

Experimental demonstration of arbitrary Raman gain-profile designs using machine learning

A machine learning framework for Raman amplifier design is experimentally tested. Performance in terms of maximum error over the gain profile is investigated for various fiber types and lengths, demonstrating highly-accurate designs

Inverse System Design Using Machine Learning: The Raman Amplifier Case

A wide range of highly-relevant problems in programmable and integrated photonics, optical amplification, and communication deal with inverse system design. Typically, a desired output (usually a gain profile, a noise profile, a transfer function or a similar continuous function) is given and the goal is to determine the corresponding set of input parameters (usually a set of input voltages, currents, powers, and wavelengths). We present a novel method for inverse system design using machine learning and apply it to Raman amplifier design. Inverse system design for Raman amplifiers consists of selecting pump powers and wavelengths that would result in a targeted gain profile. This is a challenging task due to highly-complex interaction between pumps and Raman gain. Using the proposed framework, highly-accurate predictions of the pumping setup for arbitrary Raman gain profiles are demonstrated numerically in C and C+L-band, as well as experimentally in C band, for the first time. A low mean (0.46 and 0.35 dB) and standard deviation (0.20 and 0.17 dB) of the maximum error are obtained for numerical (C+L-band) and experimental (C-band) results, respectively, when employing 4 pumps and 100 km span length. The presented framework is general and can be applied to other inverse problems in optical communication and photonics in general