Data-Driven Erbium-doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression
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- Data-Driven Erbium-doped Fiber Amplifier Gain
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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.
| Iaith wreiddiol | Saesneg |
|---|---|
| Tudalennau (o-i) | 1097-1100 |
| Nifer y tudalennau | 4 |
| Cyfnodolyn | IEEE Photonics Technology Letters |
| Cyfrol | 36 |
| Rhif y cyfnodolyn | 18 |
| Dyddiad ar-lein cynnar | 9 Awst 2024 |
| Dynodwyr Gwrthrych Digidol (DOIs) | |
| Statws | E-gyhoeddi cyn argraffu - 9 Awst 2024 |