Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
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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 dB^2) and different physical units of the same make (generalization MSE ≤ 0.06 dB^2).
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