We propose an autoencoding sequence-based transceiver for communication over
dispersive channels with intensity modulation and direct detection (IM/DD),
designed as a bidirectional deep recurrent neural network (BRNN). The receiver
uses a sliding window technique to allow for efficient data stream estimation.
We find that this sliding window BRNN (SBRNN), based on end-to-end deep
learning of the communication system, achieves a significant bit-error-rate
reduction at all examined distances in comparison to previous block-based
autoencoders implemented as feed-forward neural networks (FFNNs), leading to an
increase of the transmission distance. We also compare the end-to-end SBRNN
with a state-of-the-art IM/DD solution based on two level pulse amplitude
modulation with an FFNN receiver, simultaneously processing multiple received
symbols and approximating nonlinear Volterra equalization. Our results show
that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training
fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep
learning-based systems for communication over nonlinear channels with memory,
such as the optical IM/DD fiber channel.

本研究提出一种基于序列自编码收发器的双向深度循环神经网络（BRNN)，该网络使用滑动窗口技术来实现高效数据流估计。我们发现，这种滑动窗口 BRNN 相比之前的基于块的前馈神经网络（FFNN）自编码器在所有检测的距离上都能显著降低误码率，从而增加传输距离。与 FFNN 接收器实现的基于两级脉冲幅度调制的最新 IM/DD 方案相比较，我们的结果表明，在训练更少的参数的情况下，SBRNN 在 42 和 84 Gb /s 下均优于这些系统。我们的新型 SBRNN 设计旨在为具有存储器的非线性信道（例如光学 IM/DD 光纤信道）的端对端深度学习系统进行定制。