End-to-End Deep Learning of Optical Fiber Communications

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 th...

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Veröffentlicht in:Journal of lightwave technology Jg. 36; H. 20; S. 4843 - 4855
Hauptverfasser: Karanov, Boris, Chagnon, Mathieu, Thouin, Felix, Eriksson, Tobias A., Bulow, Henning, Lavery, Domanic, Bayvel, Polina, Schmalen, Laurent
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.10.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Communication systems
Communications systems
Computer networks
Computer simulation
Deep learning
detection
Equalization
Error correction
Machine learning
modulation
neural networks
Optical communication
optical fiber communication
Optical fibers
Optical transmitters
Optimization
Pulse amplitude modulation
Receivers
Reconfiguration
Training
Transceivers
Transmitters
ISSN:0733-8724, 1558-2213
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Zusammenfassung: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.
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ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2018.2865109