End-to-end Deep Learning of Optical Fiber Communications

TL;DR

This paper demonstrates the feasibility of using end-to-end deep neural networks to optimize optical fiber communication systems, achieving high data rates and low error rates over long distances, verified through simulations and experiments.

Contribution

It introduces a novel end-to-end deep learning approach for optical fiber transceivers, optimizing the entire system jointly and verifying its effectiveness in real-world experiments.

Findings

Achieved 42 Gb/s data rate below FEC threshold at 40 km

Outperformed conventional PAM2/PAM4 solutions with FFE

Developed a robust training method for flexible transceivers

Abstract

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 2 and 4 level pulse amplitude modulation (PAM2/PAM4) with feedforward equalization (FFE) at the receiver. Our study is the first step towards end-to-end deep learning-based optimization of optical fiber communication systems.