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Transfer Learning-based ROADM EDFA Wavelength Dependent Gain Prediction Using Minimized Data Collection

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Abstract

We implement and test transfer learning-based gain models across 16 ROADM EDFAs, which achieve less than 0.17/0.30 dB mean absolute error for booster/pre-amplifier gain prediction using only 0.5% of the full target EDFA dataset.

© 2023 The Author(s)

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