An optical communication's perspective on machine learning and its applications
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in
recent years. ML applications in optical communications and networking are also gaining …
recent years. ML applications in optical communications and networking are also gaining …
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 …
End-to-end deep learning of optical fiber communications
In this paper, we implement an optical fiber communication system as an end-to-end deep
neural network, including the complete chain of transmitter, channel model, and receiver …
neural network, including the complete chain of transmitter, channel model, and receiver …
Nonlinear interference mitigation via deep neural networks
C Häger, HD Pfister - Optical fiber communication conference, 2018 - opg.optica.org
Nonlinear Interference Mitigation via Deep Neural Networks Page 1 W3A.4.pdf OFC 2018 © OSA
2018 Nonlinear Interference Mitigation via Deep Neural Networks Christian Häger(1,2) and …
2018 Nonlinear Interference Mitigation via Deep Neural Networks Christian Häger(1,2) and …
Physics-based deep learning for fiber-optic communication systems
C Häger, HD Pfister - IEEE Journal on Selected Areas in …, 2020 - ieeexplore.ieee.org
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 …
Applying neural networks in optical communication systems: Possible pitfalls
TA Eriksson, H Bülow, A Leven - IEEE Photonics Technology …, 2017 - ieeexplore.ieee.org
We investigate the risk of overestimating the performance gain when applying neural
network-based receivers in systems with pseudorandom bit sequences or with limited …
network-based receivers in systems with pseudorandom bit sequences or with limited …
Photonic machine learning implementation for signal recovery in optical communications
Abstract Machine learning techniques have proven very efficient in assorted classification
tasks. Nevertheless, processing time-dependent high-speed signals can turn into an …
tasks. Nevertheless, processing time-dependent high-speed signals can turn into an …
Machine learning for optical fiber communication systems: An introduction and overview
Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …
data. When information is extracted from these data, reconfigurable network elements and …
Machine learning for 100 Gb/s/λ passive optical network
Responding to the growing bandwidth demand by emerging applications, such as fixed-
mobile convergence for fifth generation (5G) and beyond 5G, 100 Gb/s/λ access network …
mobile convergence for fifth generation (5G) and beyond 5G, 100 Gb/s/λ access network …
Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems
We investigate the application of dynamic deep neural networks for nonlinear equalization
in long haul transmission systems. Through extensive numerical analysis we identify their …
in long haul transmission systems. Through extensive numerical analysis we identify their …