AI-based modeling and monitoring techniques for future intelligent elastic optical networks
With the development of 5G technology, high definition video and internet of things, the
capacity demand for optical networks has been increasing dramatically. To fulfill the capacity …
capacity demand for optical networks has been increasing dramatically. To fulfill the capacity …
Machine learning models for estimating quality of transmission in DWDM networks
RM Morais, J Pedro - Journal of Optical Communications and …, 2018 - ieeexplore.ieee.org
It is estimated that 5G and the Internet of Things (IoT) will impact traffic, both in volume and
dynamicity, at unprecedented rates. Thus, to cost-efficiently accommodate these challenging …
dynamicity, at unprecedented rates. Thus, to cost-efficiently accommodate these challenging …
Machine learning-aided optical performance monitoring techniques: A review
DK Tizikara, J Serugunda, A Katumba - Frontiers in Communications …, 2022 - frontiersin.org
Future communication systems are faced with increased demand for high capacity, dynamic
bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have …
bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have …
Machine learning based linear and nonlinear noise estimation
Operators are pressured to maximize the achieved capacity over deployed links. This can be
obtained by operating in the weakly nonlinear regime, requiring a precise understanding of …
obtained by operating in the weakly nonlinear regime, requiring a precise understanding of …
Optical performance monitoring of multiple parameters in future optical networks
D Wang, H Jiang, G Liang, Q Zhan… - Journal of Lightwave …, 2020 - ieeexplore.ieee.org
Optical performance monitoring (OPM) is an important tool to facilitate the management of
future optical fiber communication networks. OPM measures the states of the physical layer …
future optical fiber communication networks. OPM measures the states of the physical layer …
Machine-learning-based telemetry for monitoring long-haul optical transmission impairments: methodologies and challenges
Current management of optical communication systems is conservative, manual-based, and
time-consuming. To improve this situation, building an intelligent closed-loop control system …
time-consuming. To improve this situation, building an intelligent closed-loop control system …
Joint estimation of linear and nonlinear coherent optical fiber signal-to-noise ratio
This letter proposes an estimator based on the neural network (NN) to jointly estimate the
linear and nonlinear signal-to-noise ratios. The proposed NN-based estimator utilizes new …
linear and nonlinear signal-to-noise ratios. The proposed NN-based estimator utilizes new …
Nonlinear SNR estimation based on the data augmentation-assisted DNN with a small-scale dataset
Fiber nonlinearity is one of the major impairments for long-haul transmission systems.
Therefore, estimating the nonlinear signal-to-noise ratio (SNR_NL) is indispensable to …
Therefore, estimating the nonlinear signal-to-noise ratio (SNR_NL) is indispensable to …
Practical considerations for near-zero margin network design and deployment
Maximizing the delivered capacity of optical transponders by mining the SNR margin to a
near-zero level is of critical importance for the economic viability of future optical networks …
near-zero level is of critical importance for the economic viability of future optical networks …
Constellation-based identification of linear and nonlinear OSNR using machine learning: a study of link-agnostic performance
We demonstrate accurate estimation of generalized optical signal to noise ratio (GOSNR) for
wavelength division multiplexed fiber communication systems using an experimentally …
wavelength division multiplexed fiber communication systems using an experimentally …