Optical Fiber Communication Systems Based on End-to-End Deep Learning

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May 18, 2020
Boris Karanov, Mathieu Chagnon, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen
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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)  
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