Optical Fiber Communication Systems Based on End-to-End Deep Learning
arXiv:2005.08785
Abstract
We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels.
Invited paper at IEEE Photonics Conference (IPC), Special Symposium for Machine Learning in Photonic Systems (SS MLPS)