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

• URL: http://arxiv.org/abs/2005.08785v1
• Date: Mon, 18 May 2020 15:02:42 GMT
• Title: Optical Fiber Communication Systems Based on End-to-End Deep Learning
• Authors: Boris Karanov, Mathieu Chagnon, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen
• Abstract summary: We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. 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.
• Score: 3.157838763210381
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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