Machine learning-based Raman amplifier design

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.Comment: conferenc

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

All-optical 160 Gbit/s RZ data retiming system incorporating a pulse shaping fibre Bragg grating

We characterize a 160Gbit/s retimer based on flat-topped pulses shaped using a superstructured fibre Bragg grating. The benefits of using shaped rather than conventional pulse forms in terms of timing jitter reduction are confirmed by bit-error-rate measurements

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

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

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

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

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

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