Abstract

High-accuracy physical layer models enable intelligent, self-driving optical networks. The dynamic wavelength-dependent gain characteristics of erbium-doped fiber amplifiers (EDFAs) remain a crucial problem in terms of modeling. The gain model directly determines the power spectrum and is therefore important for estimating the optical signal-to-noise ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven models have been widely studied, but they require a large size of data for training and suffer from poor generalizability. In this paper, we derive the gain spectra of EDFAs as a simple univariable linear function; then, based on it, we propose a gray-box EDFA gain modeling scheme. Experimental results show that, for automatic gain control (AGC) and automatic power control (APC) EDFAs, our model built with 8 data samples can achieve better performance than the neural network (NN) based model built with 900 data samples, which means the required data size for modeling can be reduced by at least 2 orders of magnitude. Moreover, in the experiment, the proposed model demonstrates superior generalizability to unseen scenarios since it is based on the underlying physics of EDFAs. With the proposed scheme, building a customized digital twin of each EDFA in optical networks becomes more feasible, which is essential, especially for next-generation multiband network operations.

© 2023 Optica Publishing Group

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