Cascaded Learning refers to a machine learning approach where multiple models are trained and applied sequentially, with the output of one model serving as the input for the next. In this specific scenario, cascaded learning is employed to develop and refine component-level Erbium-Doped Fiber Amplifier (EDFA) models for predicting the optical power spectrum in multi-span optical networks.
We implement a cascaded learning framework using component-level EDFA models for optical power spectrum prediction in multi-span networks, achieving a mean absolute error of 0.17 dB across 6 spans and 12 EDFAs with only one-shot measurement.
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