Building a digital twin of EDFA: a grey-box modeling approach

TL;DR

This paper introduces a grey-box modeling approach for EDFAs that significantly reduces data requirements and improves generalizability, enabling the creation of accurate digital twins for optical networks.

Contribution

The paper presents a novel grey-box model for EDFA gain that outperforms neural networks with far less data and better generalization, based on physical principles.

Findings

Model with 8 samples outperforms neural network with 900 samples.

Reduced data requirement by at least two orders of magnitude.

Demonstrates superior generalizability to unseen scenarios.

Abstract

To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium-doped fiber amplifiers (EDFAs) remain a crucial problem in terms of modeling, as it determines optical-to-signal noise ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven models have been widely studied, but it requires a large size of data for training and suffers from poor generalizability. In this paper, we derive the gain spectra of EDFAs as a simple univariable linear function, and then based on it we propose a grey-box EDFA gain modeling scheme. Experimental results show that for both 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 two 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. The results indicate that building a customized digital twin of each EDFA in optical networks become feasible, which is essential especially for next generation multi-band network operations.