# Multi-Task Learning to Enhance Generalizability of Neural Network Equalizers in Coherent Optical Systems

arXiv: https://arxiv.org/abs/2307.05374 · HTML: https://mlsanity.com/paper/2307.05374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Nonlinear Interference Mitigation via Deep Neural Networks](https://mlsanity.com/paper/1710.06234/) (2017)
- [Compensation of Fiber Nonlinearities in Digital Coherent Systems Leveraging Long Short-Term Memory Neural Networks](https://mlsanity.com/paper/2001.11802/) (2020)
- [Transfer Learning for Neural Networks-based Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2104.05081/) (2021)
- [Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems](https://mlsanity.com/paper/2202.12689/) (2022)
- [Knowledge Distillation Applied to Optical Channel Equalization: Solving the Parallelization Problem of Recurrent Connection](https://mlsanity.com/paper/2212.04569/) (2022)

## Abstract (arXiv)

For the first time, multi-task learning is proposed to improve the flexibility of NN-based equalizers in coherent systems. A "single" NN-based equalizer improves Q-factor by up to 4 dB compared to CDC, without re-training, even with variations in launch power, symbol rate, or transmission distance.