LDBP
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chaeger
Physics-Based Deep Learning for Fiber-Optic Communication Systems
LDBP: Learned Digital Backpropagation
Getting Started
The code is based on TensorFlow 1.13.1 and may
not work properly with other (older or newer) versions. It is recommended to create a
dedicated conda environment using the YAML file in the folder conda as
follows:
(base)~$ conda env create -f ldbp_env.yml
(base)~$ conda activate ldbp_env
Afterwards, it should be possible to run the provided jobscripts in the folder ldbp. For example:
(ldbp_env)~$ ./jobscript_isit
To train for different scenarios, most of the parameters and training options are set in a configuration file located in the folder config.
Additional Information
This repository is based on joint work with Henry D. Pfister. If you decide to use the source code for your research, please make sure to cite our paper(s):
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C. Häger and H. D. Pfister, "Physics-Based Deep Learning for Fiber-Optic Communication Systems", in IEEE J. Sel. Areas Commun. (to appear), 2020
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C. Häger and H. D. Pfister, "Nonlinear Interference Mitigation via Deep Neural Networks", in Proc. Optical Fiber Communication Conf. (OFC), San Diego, CA, March 2018
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C. Häger and H. D. Pfister, "Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communication", In Proc. IEEE Int. Symp. on Information Theory (ISIT), Vail, CO, June 2018
chaeger