# Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices

arXiv: https://arxiv.org/abs/2009.05326 · HTML: https://mlsanity.com/paper/2009.05326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain](https://mlsanity.com/paper/2308.02233/) (2023)
- [Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum](https://mlsanity.com/paper/2507.21728/) (2025)

## Abstract (arXiv)

We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements. The model shows low gain-prediction error for both the same device used for training (MSE ≤ 0.04 dB2) and different physical units of the same make (generalization MSE ≤ 0.06 dB2).