Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum

• URL: http://arxiv.org/abs/2507.21728v1
• Date: Tue, 29 Jul 2025 12:05:31 GMT
• Title: Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
• Authors: Agastya Raj, Zehao Wang, Tingjun Chen, Daniel C Kilper, Marco Ruffini,
• Abstract summary: Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optical network performance.<n>In this work, we propose a few-shot transfer learning architecture based on Semi-Supervised Self-Normalizing Neural Network (SS-NN)<n>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 loss.
• Score: 10.312656900656016
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 or 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, preamplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between 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 measurements requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.

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