Self-normalizing neural network, enabling one shot transfer learning for modeling EDFA wavelength dependent gain
A. Arockia Bazil Raj, Zhipeng Wang, Frank Slyne, Tao Chen, Daniel C. Kilper, Marco Ruffini
Abstract
We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.
Topics & Concepts
Computer scienceOptical amplifierArtificial neural networkWavelengthArtificial intelligenceOptoelectronicsMaterials scienceLaserPhysicsOpticsAdvanced Optical Sensing TechnologiesSemiconductor Lasers and Optical DevicesAdvanced Photonic Communication Systems