Combined modulation format identification and optical signal-to-noise ratio monitoring with high accuracy and generalizability based on a proposed fused module few-shot learning algorithm in dynamic coherent optical transmissions

Abstract

Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are important portions of optical performance monitoring (OPM) for future dynamic optical networks. In this paper, we proposed a fusion module few-shot learning (FMFSL) algorithm as an improvement upon the ordinary few-shot learning algorithms for image recognition with the specialty in adopting a combination of a dilated convolutional group and an asymmetric convolutional group to advance the feature extraction. FMFSL algorithm is applied in MFI and OSNR monitoring in coherent optical communication systems with its performance investigated in both back-to-back and fiber transmission scenarios using small-scale constellation diagrams. The results show that FMFSL algorithm can achieve 100% accuracy in MFI and higher OSNR monitoring accuracy compared to the few-shot learning algorithms Deep Nearest Neighbor Neural Network (DN4) and Prototypical Nets (PN) with 2.14% and 4.28% for 64QAM and 3.38% and 8.06% for 128QAM, respectively, without much increase in time consumption. Furthermore, the trained FMFSL algorithm remains excellent in MFI and OSNR monitoring without retraining while employed in back-to-back transmission scenarios with smaller OSNR intervals and fiber transmission scenarios with different amounts of Kerr nonlinearity, demonstrating its high capabilities in generalization and robustness. FMFSL algorithm provides a potential solution for OPM in future dynamic optical networks as a novel machine learning tool.