PyTorch examples shown in the ECOC 2020 Invited Talk "End-to-end Learning in Optical Fiber Communications: Concept and Transceiver Design"
In this repository, you can find some of the examples that are shown in our ECOC 2020 presentation called "End-to-end Learning in Optical Fiber Communications: Concept and Transceiver Design" by Boris Karanov, Polina Bayvel and Laurent Schmalen.
Additionally, you can find the slides accompanying the presentation (for background information) in the root directory.
Please cite as:
- Boris Karanov, Polina Bayvel, and Laurent Schmalen, "End-to-end Learning in Optical Fiber Communications: Concept and Transceiver Design," Proc. European Conference on Optical Communications (ECOC), Brussels, Belgium, Dec. 2020
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.