# End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks

## TL;DR

This paper introduces a bidirectional recurrent neural network-based transceiver for dispersive optical channels, significantly improving bit-error-rate and transmission distance compared to traditional methods through end-to-end deep learning.

## Contribution

It presents a novel sliding window bidirectional RNN transceiver that outperforms existing autoencoders and IM/DD solutions in optical communication systems with nonlinear channels.

## Key findings

- Achieves lower bit-error-rate at various distances.
- Outperforms state-of-the-art IM/DD systems at 42 and 84 Gb/s.
- Uses fewer parameters than comparable neural network models.

## Abstract

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.