429 citations · 806 across the 12 of their papers we have counts for
5 papers · 1 filter
Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning
Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov +4
In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated. By embedding a differentiable fiber channel model within two neural netw…
Coexistence of Continuous Variable Quantum Key Distribution and 7 12.5 Gbit/s Classical Channels
Tobias A. Eriksson, Takuya Hirano, Motoharu Ono +6
We study coexistence of CV-QKD and 7 classical 12.5 Gbit/s on-off keying channels in WDM transmission over the C-band. We demonstrate key generation with a distilled secret key rat…
Deep Learning of Geometric Constellation Shaping including Fiber Nonlinearities
Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov +1
A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects wit…
End-to-end Deep Learning of Optical Fiber Communications
Boris Karanov, Mathieu Chagnon, Félix Thouin +5
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. Th…
Exploring and Experimenting with Shaping Designs for Next-Generation Optical Communications
Fanny Jardel, Tobias A. Eriksson, Cyril Méasson +4
A class of circular 64-QAM that combines 'geometric' and 'probabilistic' shaping aspects is presented. It is compared to square 64-QAM in back-to-back, single-channel, and WDM tran…