An ultra-fast method for gain and noise prediction of Raman amplifiers

A machine learning method for prediction of Raman gain and noise spectra is presented: it guarantees high-accuracy (RMSE < 0.4 dB) and low computational complexity making it suitable for real-time implementation in future optical networks controllers

Machine learning applied to inverse systems design

In this work, we will give an overview of some of the most recent and successful applications of machine learning based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems

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 paper, 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.Comment: 11 pages, 12 figure

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 ~0.3dB. This framework provides the comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of the next-generation optical communication systems, expected to employ Raman amplification.Comment: 4 pages, 5 figure

Fiber-Agnostic Machine Learning-Based Raman Amplifier Models

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 NN model can predict the gain profile of a new fiber type (unseen by the model during training) with a maximum absolute error as low as 0.22 dB. We show that this generalization is only possible when the unseen fiber parameters are similar to the fibers used to build the model. Therefore, a training dataset with a wide range of optical fibers parameters is needed to enhance the chance of accurately predicting the gain of a new fiber. This implies that time-consuming experimental measurements of various fiber types can be avoided. For that, here we extend and improve our general model by numerically generating the dataset. By doing so, it is possible to generate uniformly distributed data that covers a wide range of optical fiber types. The results show that the averaged maximum prediction error is reduced when compared to the limited experimental data-based general models. As the second and final contribution of this work, we propose the use of transfer learning (TL) to re-train the numerical data-based general model using just a few experimental measurements. Compared with the fiber-specific models, this TL-upgraded general model reaches very similar accuracy, with just 3.6% of the experimental data . These results demonstrate that the already fast and accurate NN-based RA models can be upgraded to have strong fiber generalization capabilities

ML-Based Spectral Power Profiles Prediction in Presence of ISRS for Ultra-Wideband Transmission

A generalized method based on machine learning (ML) and artificial neural networks (ANNs) is proposed for a fast and accurate prediction of spectral and spatial evolution of power profiles in support of performance and quality-of-transmission (QoT) real-time assessment of ultra-wideband links. These systems, operating on bandwidths larger than the standard C–band, are affected by inter-channel stimulated Raman scattering (ISRS), whose impact on power profiles evolution along the fiber is generally estimated by solving numerically a set of nonlinear ordinary differential equations (ODEs). However, the computational effort, in terms of complexity and convergence time to the solution, increases with the bandwidth and the number of transmitted wavelength division multiplexing (WDM) channels, which makes the usual approach no longer particularly suitable to operate in real time. To meet the speed requirements, three different ANNs are introduced to make fast predictions of power profiles over frequency and distance considering a wide range of scenarios: different power per channel values, different fiber types and different span lengths. Two ANNs are used on synthetic data to estimate the impact of linear and nonlinear fiber impairments in support of system modeling. Specifically, one to directly predict the evolution of spectral power profiles along the fiber and the other to estimate the coefficients to insert in a closed-form version of the EGN model. A third ANN operates on experimental data and it is used to predict power profiles at the end of the fiber for fast estimations of system performance. The obtained results show highly accurate predictions with values of maximum absolute error, computed between predicted and actual power profiles, not exceeding 0.2 dB for ∼97% of cases for synthetic data and always below 0.5 dB for experimental data. Such results prove the potential of the proposed approach making it suitable for real time application of QoT estimation

Multi-band programmable gain Raman amplifier

Optical communication systems, operating in C-band, are reaching their theoretically achievable capacity limits. An attractive and economically viable solution to satisfy the future data rate demands is to employ the transmission across the full low-loss spectrum encompassing O, E, S, C and L band of the single mode fibers (SMF). Utilizing all five bands offers a bandwidth of up to ~53.5 THz (365 nm) with loss below 0.4 dB/km. A key component in realizing multi-band optical communication systems is the optical amplifier. Apart from having an ultra-wide gain profile, the ability of providing arbitrary gain profiles, in a controlled way, will become an essential feature. The latter will allow for signal power spectrum shaping which has a broad range of applications such as the maximization of the achievable information rate × distance product, the elimination of static and lossy gain flattening filters (GFF) enabling a power efficient system design, and the gain equalization of optical frequency combs. In this paper, we experimentally demonstrate a multi-band (S+C+L) programmable gain optical amplifier using only Raman effects and machine learning. The amplifier achieves >1000 programmable gain profiles within the range from 3.5 to 30 dB, in an ultra-fast way and a very low maximum error of 1.6⋅10−2 dB/THz over an ultra-wide bandwidth of 17.6-THz (140.7-nm

Machine Learning Applications to Optical Communication Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen