Active Meta-Learning for Few-Shot QoT Estimation in Optical Networks

Abstract

Accurate quality of transmission (QoT) estimation, particularly generalized signal-to-noise ratio (GSNR) prediction, in newly deployed C+L-band optical networks is hindered by data scarcity and incomplete physical link information, leading to a cold-start problem for conventional deep learning methods. To address this issue, we propose PAL-MISA, a data-efficient framework combining Parameterized Active Learning (PAL) and Meta-Initialized Sparse Adaptation (MISA). PAL learns to select high-value measurements, while MISA provides a transferable initialization for rapid sparse adaptation to unseen physical environments. Validated on three representative network topologies, PAL-MISA accelerates convergence and reduces performance fluctuations compared with conventional training from scratch in the target domain. To achieve the same prediction error, PAL-MISA reduces the required measurement data volume by 80% and ultimately achieves a minimum mean absolute error (MAE) of 0.04 dB, offering a robust solution for digital-twin deployment in uncharacterized optical networks.