Related papers: Improved Physics-based Raman Amplifier Model in C+…
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression. Higher accuracy, less data requirements and lower computational complexity are demonstrated…
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…
We propose a transfer learning-enabled Transformer framework to simultaneously realize accurate modeling and Raman pump design in C+L-band systems. The RMSE for modeling and peak-to-peak GSNR variation/deviation is within 0.22 dB and…
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…
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…
We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution, both in distance along the fiber and…
We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addresses the amplification in…
We experimentally validate a real-time machine learning framework, capable of controlling the pump power values of Raman amplifiers to shape the signal power evolution in two-dimensions (2D): frequency and fiber distance. In our setup,…
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.
The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of…
We propose a novel input refinement methodology incorporating sensitivity analysis and memory-aware weighting for jointly refining numerous diverse inputs. Field trials show ~2.5 dB and ~2.3 dB improvements in Q-factor and power estimation,…
Any amplifier requires coupling to its internal degrees of freedom for energy gain. This coupling introduces extra quantum noise to the output. On the other hand, if the internal degree of the amplifier can be accessed and manipulated, we…
The accuracy of a recently-developed closed-form GN nonlinear interference model is evaluated in experimental 1065 km S+C+L band WDM transmission with backward Raman pumping. The model accurately estimates the nonlinear interference and ASE…
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.
The current evolution towards a massive number of antennas and a large variety of transceiver architectures forces to revisit the conventional techniques used to improve the fundamental power amplifier (PA) linearity-efficiency trade-off.…
This paper presents an efficient numerical method for calculating spatial power profiles of both signal and pump with significant Interchannel Stimulated Raman Scattering (ISRS) and backward Raman amplification in multiband systems. This…
We investigate C+L+S long-haul systems using a closed-form GN/EGN non-linearity model. We perform accurate launch power and Raman pump optimization. We show a potential 4x throughput increase over legacy C-band systems in 1000 km links,…
Parametric amplification is widely used in nanoelectro-mechanical systems to enhance the transduced mechanical signals. Although parametric amplification has been studied in different mechanical resonator systems, the nonlinear dynamics…
In a normal indoor environment, Raman spectrum encounters noise often conceal spectrum peak, leading to difficulty in spectrum interpretation. This paper proposes deep learning (DL) based noise reduction technique for Raman spectroscopy.…
Raman spectroscopy is a powerful and non-invasive method for analysis of chemicals and detection of unknown substances. However, Raman signal is so weak that background noise can distort the actual Raman signal. These baseline shifts that…