Distance and spectral power profile shaping using machine learning enabled Raman amplifiers

We propose a Convolutional Neural Network (CNN) to learn the mapping between the 2D power profiles, (distance and frequency), and the Raman pumps. Using the CNN, the pump powers and wavelengths for arbitrary 2D profiles can be determined with high accuracy

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

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 power evolution in both distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54, and 0.64 dB) and standard deviation set (0.62, 0.43, and 0.38 dB) of the maximum test error are obtained numerically employing two and three counter-, and four bidirectional propagating pumps, respectively

Dual-polarization nonlinear Fourier transform-based optical communication system

New services and applications are causing an exponential increase in Internet traffic. In a few years, the current fiber optic communication system infrastructure will not be able to meet this demand because fiber nonlinearity dramatically limits the information transmission rate. Eigenvalue communication could potentially overcome these limitations. It relies on a mathematical technique called “nonlinear Fourier transform (NFT)” to exploit the “hidden” linearity of the nonlinear Schrödinger equation as the master model for signal propagation in an optical fiber. We present here the theoretical tools describing the NFT for the Manakov system and report on experimental transmission results for dual polarization in fiber optic eigenvalue communications. A transmission of up to 373.5 km with a bit error rate less than the hard-decision forward error correction threshold has been achieved. Our results demonstrate that dual-polarization NFT can work in practice and enable an increased spectral efficiency in NFT-based communication systems, which are currently based on single polarization channels

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

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

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

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

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

Characterising plasticised cellulose acetate-based historic artefacts by NMR spectroscopy: a new approach for quantifying the degree of substitution and diethyl phthalate contents

As one of the first semi-synthetic plastics produced industrially, cellulose acetate (CA)-based artefacts represent valued items in museum collections and archives which, however, present stability issues. High temperature and relative humidity conditions have long been known to promote changes in CA properties, for instance, due to the deacetylation of CA polymer chains and the loss of plasticiser from the polymer matrix. However, there is a need for improved methods for the quantification of plasticiser loss and CA deacetylation. In this context, this contribution presents a new approach for enabling the investigation of plasticiser loss and deacetylation degradation processes in historic plasticised CA-based artefacts which is based on high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR). The proposed methods allow for simple and fast quantification of diethyl phthalate contents and average degree of substitution (DS), while requiring no need for extractive separation between the plasticiser and the CA polymer matrix prior to analysis. Both methods are demonstrated by their application towards a series of reference samples, historic artefacts and artificially aged plasticised CA materials. Our analysis indicates that plasticiser content and DS can be accurately quantified by using high-resolution 1H NMR and both methods have been compared to analyses performed using infrared spectroscopy

Nanocellulose/fullerene hybrid films assembled at the air/water interface as promising functional materials for photo-electrocatalysis

Cellulose nanomaterials have been widely investigated in the last decade, unveiling attractive properties for emerging applications. The ability of sulfated cellulose nanocrystals (CNCs) to guide the supramolecular organization of amphiphilic fullerene derivatives at the air/water interface has been recently highlighted. Here, we further investigated the assembly of Langmuir hybrid films that are based on the electrostatic interaction between cationic fulleropyrrolidines deposited at the air/water interface and anionic CNCs dispersed in the subphase, assessing the influence of additional negatively charged species that are dissolved in the water phase. By means of isotherm acquisition and spectroscopic measurements, we demonstrated that a tetra-sulfonated porphyrin, which was introduced in the subphase as anionic competitor, strongly inhibited the binding of CNCs to the floating fullerene layer. Nevertheless, despite the strong inhibition by anionic molecules, the mutual interaction between fulleropyrrolidines at the interface and the CNCs led to the assembly of robust hybrid films, which could be efficiently transferred onto solid substrates. Interestingly, ITO-electrodes that were modified with five-layer hybrid films exhibited enhanced electrical capacitance and produced anodic photocurrents at 0.4 V vs Ag/AgCl, whose intensity (230 nA/cm2) proved to be four times higher than the one that was observed with the sole fullerene derivative (60 nA/cm2)