# Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems

arXiv: https://arxiv.org/abs/2202.12689 · HTML: https://mlsanity.com/paper/2202.12689/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020)
- [Performance versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2103.08212/) (2021)
- [Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2104.05081/) (2021)
- [Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation](https://mlsanity.com/paper/1911.10131/) (2019)

## Built on by (most cited first, 4 of 4)

- [Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation](https://mlsanity.com/paper/2208.12866/) (2022)
- [Computational Complexity Evaluation of Neural Network Applications in Signal Processing](https://mlsanity.com/paper/2206.12191/) (2022)
- [Implementing Neural Network-Based Equalizers in a Coherent Optical Transmission System Using Field-Programmable Gate Arrays](https://mlsanity.com/paper/2212.04703/) (2022)
- [Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2307.05374/) (2023)

## Abstract (arXiv)

We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99% training process reduction, which we demonstrate in three experimental setups.