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

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

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

Machine learning enabled Raman amplifiers

Ultra-wideband (UWB) optical communication systems, envision to operate in O+E+S+C+l band, are a viable solution to cope with the network’s exponential traffic growth [1] . One of the main challenges to provide beyond C-band transmission is a lack of optical amplifiers. Since the erbium-doped fiber amplifiers (EDFAs) are limited to C and L bands only, new technologies will have to be explored to cover the remaining bands. Some examples of amplifiers able to provide amplification beyond C–band are: bismuth doped fibre amplifiers (BDFA) [2] , semiconductor optical amplifiers, (SOAs) [3] and Raman amplifiers (RAs) [4] . Compared to the solutions based on BDFA and SOA, optical amplifiers based RAs offer a higher degree of commercial maturity [5] . Most importantly, RA amplifiers can provide gain in any band provided a proper allocation of pump powers and wavelength

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

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

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

Water sorption and diffusion in cellulose acetate: The effect of plasticisers

The conservation of cellulose acetate plastics in museum collections presents a significant challenge, due to the material's instability. Several studies have led to an understanding of the role of relative humidity (RH) and temperature in the decay process. It is well established that the first decay mechanism in cellulose acetate museum objects is the loss of plasticiser, and that the main decay mechanism of the polymer chain involves hydrolysis reactions. This leads to the loss of sidechain groups and the breakdown of the main polymer backbone. However, interactions between these decay mechanisms, specifically the way in which the loss of plasticiser can modify the interaction between cellulose acetate and water, has not yet been investigated. This research addresses the role of RH, studying the sorption and diffusion of water in cellulose acetate and how this interaction can be affected by plasticiser concentration using Dynamic Vapour Sorption (DVS)

Unveiling the importance of diffusion on the deterioration of cellulose acetate artefacts: The profile of plasticiser loss as assessed by infrared microscopy

Cellulose acetate (CA) artefacts are one of the most valued plastic items in museum collections and are known to present stability issues, with the loss of plasticiser being among the main degradation processes. This study investigates the concentration distribution of diethyl phthalate (DEP) plasticiser throughout the dimensions of CA using infrared microscopy for the first time. Artificial ageing experiments using reference and historic CA plasticised with DEP were performed to assess the change in the concentration profiles as a function of ageing time. Our analysis indicates that the plasticiser loss from CA artefacts is likely controlled by its diffusion, resulting in a concentration gradient in which lower plasticiser contents are observed at the external layers of the material