Photonic neuromorphic technologies in optical communications
A Argyris - Nanophotonics, 2022 - degruyter.com
Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-
solving in many applications. Such approaches found fertile ground in optical …
solving in many applications. Such approaches found fertile ground in optical …
Physics-based deep learning for fiber-optic communication systems
We propose a new machine-learning approach for fiber-optic communication systems
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …
whose signal propagation is governed by the nonlinear Schrödinger equation (NLSE). Our …
Advanced convolutional neural networks for nonlinearity mitigation in long-haul WDM transmission systems
Practical implementation of digital signal processing for mitigation of transmission
impairments in optical communication systems requires reduction of the complexity of the …
impairments in optical communication systems requires reduction of the complexity of the …
Digital longitudinal monitoring of optical fiber communication link
Optical transmission links are generally composed of optical fibers, optical amplifiers, and
optical filters. In this paper, we present a channel reconstruction method (CRM) that extracts …
optical filters. In this paper, we present a channel reconstruction method (CRM) that extracts …
Deep learning based digital backpropagation demonstrating SNR gain at low complexity in a 1200 km transmission link
A deep learning (DL) based digital backpropagation (DBP) method with a 1 dB SNR gain
over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission …
over a conventional 1 step per span DBP is demonstrated in a 32 GBd 16QAM transmission …
Model-based machine learning for joint digital backpropagation and PMD compensation
In this article, we propose a model-based machine-learning approach for dual-polarization
systems by parameterizing the split-step Fourier method for the Manakov-PMD equation …
systems by parameterizing the split-step Fourier method for the Manakov-PMD equation …
Design and analysis of recurrent neural networks for ultrafast optical pulse nonlinear propagation
In this work, we analyze different types of recurrent neural networks (RNNs) working under
several different parameters to best model the nonlinear optical dynamics of pulse …
several different parameters to best model the nonlinear optical dynamics of pulse …
Machine learning-based mitigation of thermal and nonlinear impairments in optical communication grids
Nonlinear impairments (NIs) act as limiting factors in the performance of long-haul optical
communication grids (OCGs), particularly when operating at 100 Gbps over many channels …
communication grids (OCGs), particularly when operating at 100 Gbps over many channels …
Combined neural network and adaptive DSP training for long-haul optical communications
Machine Learning (ML) algorithms have shown to complement standard digital signal
processing (DSP) tools in mitigating fiber nonlinearity and improving long-haul transmission …
processing (DSP) tools in mitigating fiber nonlinearity and improving long-haul transmission …
Perturbation theory-aided learned digital back-propagation scheme for optical fiber nonlinearity compensation
Derived from the regular perturbation treatment of the nonlinear Schrödinger equation, a
machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is …
machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is …