Real-time Transmission of Geometrically-shaped Signals using a Software-defined GPU-based Optical Receiver

no code implementations • 16 Aug 2021 • Sjoerd van der Heide, Ruben S. Luis, Sebastiaan Goossens, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yoshinari Awaji, Alex Alvarado, Hideaki Furukawa, Chigo Okonkwo

A software-defined optical receiver is implemented on an off-the-shelf commercial graphics processing unit (GPU).

Real-time 10,000 km Straight-line Transmission using a Software-defined GPU-Based Receiver

no code implementations • 16 Aug 2021 • Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo

8-, 16-, 32-, and 64-QAM are successfully transmitted over 7600, 5600, 3600, and 1600 km, respectively.

10,000 km Straight-line Transmission using a Real-time Software-defined GPU-Based Receiver

no code implementations • 8 Apr 2021 • Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo

Real-time operation of a software-defined, GPU-based optical receiver is demonstrated over a 100-span straight-line optical link.

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Field Trial of a Flexible Real-time Software-defined GPU-based Optical Receiver

no code implementations • 27 Nov 2020 • Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yohinari Awaji, Hideaki Furukawa, Chigo Okonkwo

We introduce a flexible, software-defined real-time multi-modulation format receiver implemented on an off-the-shelf general-purpose graphics processing unit (GPU).

Real-time, Software-Defined, GPU-Based Receiver Field Trial

no code implementations • 27 Oct 2020 • Sjoerd van der Heide, Ruben S. Luis, Benjamin J. Puttnam, Georg Rademacher, Ton Koonen, Satoshi Shinada, Yoshinari Awaji, Chigo Okonkwo, Hideaki Furukawa

We demonstrate stable real-time operation of a software-defined, GPU-based receiver over a metropolitan network.

Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning

1 code implementation • 1 Oct 2018 • Rasmus T. Jones, Tobias A. Eriksson, Metodi P. Yankov, Benjamin J. Puttnam, Georg Rademacher, Ruben S. Luis, Darko Zibar

In this paper, an unsupervised machine learning method for geometric constellation shaping is investigated.

BIG-bench Machine Learning