End-to-End Deep Learning of Optical Fiber Communications

By B. Karanov, M. Chagnon, F. Thouin, Tobias A. Eriksson, Henning Bülow, D. Lavery, Polina Bayvel, Laurent Schmalen. 2018

326 citations recorded by the source corpus.

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow—without reconfiguration—reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on two- and four-level pulse amplitude modulation with feedforward equalization at the receiver. Our study is the first step toward end-to-end deep learning based optimization of optical fiber communication systems.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “Examples of algorithm deficit include channels with complex non-linear dynamical models, such as optical links [65]; Gaussian channels with feedback, for which optimal practical encoding schemes are not known [66]; multiple access channels with sparse transmission codes [67]; and joint source-channe”
DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback (2019, background): “More impressive results have been achieved through DNNbased designs in settings where current codes fall short of the fundamental theoretical limits; for example, for channels that are harder to model, such as optical [28] and molecular [29] communications, or in settings no practical coding scheme ”