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.

本论文采用深度学习实现了一种光纤通信系统，可通过单端到端的过程优化收发器，构建出一个灵活的，鲁棒性较好的收发器，成功地将其应用于强度调制 / 直接检测（IM/DD）系统，实现了可靠的传输，并将传输速度提高至 42GBfs。