Photonics (Mar 2025)
Joint Three-Task Optical Performance Monitoring with High Performance and Superior Generalizability Using a Meta-Learning-Based Convolutional Neural Network-Attention Algorithm and Amplitude-Differential Phase Histograms Across WDM Transmission Scenarios
Abstract
Nonlinear noise power (NLNP) estimation, optical signal-to-noise ratio (OSNR) monitoring, and modulation format identification (MFI) are crucial for optical performance monitoring (OPM) in future dynamic WDM optical networks. This paper proposes an OPM scheme to simultaneously implement these three tasks in both single-channel and WDM systems by combining amplitude-differential phase histograms (ADPH) with the MAML-CNN-ATT algorithm that integrates model-agnostic meta-learning (MAML), the convolutional neural network (CNN), and the attention mechanism (ATT). The meta-learning algorithms can learn optimal initial model parameters across multiple related tasks, enabling them to quickly adapt to new tasks through fine-tuning with a small amount of data. This results in superior self-adaptability and generalizability, making them more suitable for WDM scenarios than the transfer learning (TL) algorithms. The CNN-ATT algorithm can effectively extract comprehensive features, capturing both local and global dependencies, thus improving the quality of the feature representation. The ADPH sequence data combine the amplitude information and the differential phase information that indicate the signal’s overall characteristics. The results demonstrate that the MAML-CNN-ATT algorithm achieves errors of less than 1 dB in both NLNP estimation and OSNR monitoring tasks while achieving 100% accuracy in the MFI task. It exhibits excellent OPM performance not only in the single channel but also in the WDM transmission, with only a few steps of fine-tuning. The MAML-CNN-ATT algorithm provides a solution with high performance and rapid self-adaptation for the multi-task OPM in dynamic optical networks.
Keywords
