# End-to-end Deep Learning of Optical Fiber Communications

arXiv: https://arxiv.org/abs/1804.04097 · HTML: https://mlsanity.com/paper/1804.04097/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [Trimming the Fat from OFDM: Pilot- and CP-less Communication with End-to-end Learning](https://mlsanity.com/paper/2101.08213/) (2021)
- [Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications](https://mlsanity.com/paper/2401.12800/) (2024)
- [End-to-end Waveform Learning Through Joint Optimization of Pulse and Constellation Shaping](https://mlsanity.com/paper/2106.15158/) (2021)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)
- [Learning OFDM Waveforms with PAPR and ACLR Constraints](https://mlsanity.com/paper/2110.10987/) (2021)
- [End-to-End Learning of Joint Geometric and Probabilistic Constellation Shaping](https://mlsanity.com/paper/2112.05050/) (2021)
- [Geometric Constellation Shaping for Fiber-Optic Channels via End-to-End Learning](https://mlsanity.com/paper/2211.04311/) (2022)
- [Residual-Aided End-to-End Learning of Communication System without Known Channel](https://mlsanity.com/paper/2102.10786/) (2021)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)
- [Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2405.09557/) (2024)
- [Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise](https://mlsanity.com/paper/2405.10814/) (2024)
- [Neural Probabilistic Shaping: Joint Distribution Learning for Optical Fiber Communications](https://mlsanity.com/paper/2507.16012/) (2025)
- [Semantic optical fiber communication system](https://mlsanity.com/paper/2212.14739/) (2022)
- [A Hybrid Approach combining ANN-based and Conventional Demapping in Communication for Efficient FPGA-Implementation](https://mlsanity.com/paper/2304.05042/) (2023)
- [Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments](https://mlsanity.com/paper/2310.05950/) (2023)
- [End-to-End Autoencoder for Drill String Acoustic Communications](https://mlsanity.com/paper/2405.03840/) (2024)
- [Fiber neural networks for the intelligent optical fiber communications](https://mlsanity.com/paper/2408.12602/) (2024)
- [Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey](https://mlsanity.com/paper/2609.22294/) (2026)

## Abstract (arXiv)

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.