mlsanityData to 7 Oct 2026

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

arXiv 2009.05326 ↗First posted 11 Sep 2020eess.SP cs.LG physics.opticscited by 2

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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).

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2020.

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.

  1. 2023Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gaincited by 1
  2. 2025Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrumcited by 0