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

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.

arXiv commentaccepted for publication in Optics Express

References in corpus (5)

• Deep Learning-Based Communication Over the Air
• End-to-end Deep Learning of Optical Fiber Communications
• Light Gated Recurrent Units for Speech Recognition
• Neural Network Detection of Data Sequences in Communication Systems
• Applying Neural Networks in Optical Communication Systems: Possible Pitfalls

Cited by in corpus (9)

• Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation
• Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots
• End-to-end optimization of coherent optical communications over the split-step Fourier method guided by the nonlinear Fourier transform theory
• End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
• Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning
• End-to-end Learning of a Constellation Shape Robust to Channel Condition Uncertainties
• Gradient-free training of autoencoders for non-differentiable communication channels
• An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
• On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge