PyTorch examples and presentation (pdf) shown in the talk "End-to-end Deep Learning in Optical Fiber Communications" presented at the "Spring School 2022: Emerging and Future Communication Networks: Technologies, Architectures, and Tools" in April 2022
In this repository, you can find some of the examples that are shown in our presentation called "End-to-end Deep Learning in Optical Fiber Communications" presented at the "Spring School 2022: Emerging and Future Communication Networks: Technologies, Architectures, and Tools" in Paris. Authors are Laurent Schmalen, Andrej Rode, Boris Karanov and Vincent Lauinger.
Additionally, you can find the slides accompanying the presentation (for background information) in the root directory.
The programming language Python is usually pre-installed in current Linux distributions and OSX. Additionally required modules need to be installed by hand from the packet sources. Alternatively, we highly recommend to use readily available Python distributions that are tuned for data science. One such distribution is Anaconda. Anaconda is also the preferred method to install a complete Python environment on a Windows machine. If you are using Anaconda, we advise you to create an environment within you run the notebooks. You can directly create the environment for running the notebooks using the provided environment.yml file using conda env create -f environment.yml. You can then activate the envinronment using conda activate PyTorch.
This work has received funding from the European Research Council (ERC) under the European Union's Horizon2020 research and innovation programme (grant agreement No. 101001899).