Experimental Demonstration of Eavesdropping Localization Based on Physics-Informed Neural Network

Abstract

This paper proposes a physics-informed neural network (PINN) scheme for localizing bending eavesdropping in coherent optical communication systems. First, we establish a signal transmission model under bending eavesdropping based on the Manakov equations to analyze its impact on physical information such as linear birefringence and nonlinearity. Subsequently, a PINN is developed by incorporating physical information such as linear birefringence and nonlinear effects into a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) architecture. To validate the effectiveness of the scheme, we construct an experimental platform for bending eavesdropping localization in an 80 km, 168 Gbps quadrature phase shift keying (QPSK) coherent optical communication system. Polarization data during eavesdropping are collected from nine positions with bending radii of 10.8 mm and 15 mm. The performance of three models, including CNN, CNN-BiLSTM and PINN is evaluated for bending eavesdropping localization under both bending radii. Experimental results demonstrate that the PINN achieves localization accuracies of 100% and 99.8% under bending radii of 10.8 mm and 15 mm, respectively. This work offers a novel theoretical framework and methodological approach for localizing bending eavesdropping in optical fiber communication systems.