Data-Driven Erbium-doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression
Research output: Contribution to journal › Article › peer-review
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In: IEEE Photonics Technology Letters, Vol. 36, No. 18, 15.09.2024, p. 1097-1100.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Data-Driven Erbium-doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression
AU - Harvey, Calum
AU - Faruk, Md Saifuddin
AU - Savory, Seb J.
PY - 2024/8/9
Y1 - 2024/8/9
N2 - 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 improverobustness and repeatability of convergence.
AB - 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 improverobustness and repeatability of convergence.
U2 - 10.1109/LPT.2024.3441110
DO - 10.1109/LPT.2024.3441110
M3 - Article
VL - 36
SP - 1097
EP - 1100
JO - IEEE Photonics Technology Letters
JF - IEEE Photonics Technology Letters
SN - 1041-1135
IS - 18
ER -