Data-Driven Erbium-doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression
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- Data-Driven Erbium-doped Fiber Amplifier Gain
Accepted author manuscript, 386 KB, PDF document
DOI
We propose a data-driven erbium-doped fiber amplifier (EDFA) gain model utilizing Gaussian process regression (GPR). An additive Laplacian and radial-basis function kernel is proposed for the GPR and was found to outperform deep neural network (DNN) methods while additionally providing prediction uncertainty. Performance is measured using mean absolute error (MAE) averaged across five different EDFAs with three manufacturers. The GPR achieves an MAE of 0.1 dB using 30 training samples in contrast to the DNN that achieves an MAE of 0.25 dB using 3000 training samples. Additionally, we demonstrate that active learning can be used to improve
robustness and repeatability of convergence.
robustness and repeatability of convergence.
| Original language | English |
|---|---|
| Pages (from-to) | 1097-1100 |
| Number of pages | 4 |
| Journal | IEEE Photonics Technology Letters |
| Volume | 36 |
| Issue number | 18 |
| Early online date | 9 Aug 2024 |
| DOIs | |
| Publication status | E-pub ahead of print - 9 Aug 2024 |