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 …
Digital signal processing for fiber nonlinearities
Digital signal processing for fiber nonlinearities [Invited] clickable element to expand a topic
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A silicon photonic–electronic neural network for fibre nonlinearity compensation
In optical communication systems, fibre nonlinearity is the major obstacle in increasing the
transmission capacity. Typically, digital signal processing techniques and hardware are …
transmission capacity. Typically, digital signal processing techniques and hardware are …
Field and lab experimental demonstration of nonlinear impairment compensation using neural networks
Fiber nonlinearity is one of the major limitations to the achievable capacity in long distance
fiber optic transmission systems. Nonlinear impairments are determined by the signal …
fiber optic transmission systems. Nonlinear impairments are determined by the signal …
Digital signal processing for coherent transceivers employing multilevel formats
Digital coherent transceivers have revolutionized optical fiber communications due to their
superior performance offered compared to intensity modulation and direct detection based …
superior performance offered compared to intensity modulation and direct detection based …
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 …
Beyond 100 Gb/s: capacity, flexibility, and network optimization
In this paper, we discuss building blocks that enable the exploitation of optical capacities
beyond 100 Gb/s. Optical networks will benefit from more flexibility and agility in their …
beyond 100 Gb/s. Optical networks will benefit from more flexibility and agility in their …
Advanced C+ L-band transoceanic transmission systems based on probabilistically shaped PDM-64QAM
A Ghazisaeidi, IF de Jauregui Ruiz… - Journal of Lightwave …, 2017 - ieeexplore.ieee.org
We review the most recent advanced concepts and methods employed in the cutting-edge
spectrally efficient coherent fiber-optic transoceanic transmission systems, such as …
spectrally efficient coherent fiber-optic transoceanic transmission systems, such as …
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 …