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Title: Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Accurate modeling of the gain spectrum in erbium-doped fiber amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a semi-supervised self-normalizing neural network (SS-NN) that leverages internal EDFA features—such as VOA input/output power and attenuation—to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom-weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, pre-amplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between the source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurement requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.  more » « less
Award ID(s):
2330333
PAR ID:
10655963
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
IEEE/Optica
Date Published:
Journal Name:
Journal of Optical Communications and Networking
Volume:
17
Issue:
9
ISSN:
1943-0620
Page Range / eLocation ID:
D106
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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