Adversarial learning-based knowledge distillation for optical performance monitoring in 7 core fiber

Xu Zhu; Bo Liu; Jianxin Ren; Xiaorong Zhu; Yaya Mao; Xiangyu Wu; Mingye Li; Shuaidong Chen; Yu Bai

doi:10.1117/12.2619876

26 July 2022 Adversarial learning-based knowledge distillation for optical performance monitoring in 7 core fiber

Xu Zhu, Bo Liu, Jianxin Ren, Xiaorong Zhu, Yaya Mao, Xiangyu Wu, Mingye Li, Shuaidong Chen, Yu Bai

Proceedings Volume 12278, 2021 International Conference on Optical Instruments and Technology: Optical Communication and Optical Signal Processing; 122780M (2022) https://doi.org/10.1117/12.2619876
Event: 2021 International Conference on Optical Instruments and Technology, 2022, Online Only

Abstract

An adversarial learning based knowledge distillation optical performance monitoring scheme based on has been proposed for 7 core fiber in this paper. Adversarial learning-based knowledge distillation simplified the architecture of the neural network for optical performance monitoring, including modulation format recognition (MFR) and optical signal-to-noise ratio (OSNR) estimation, in spatial division multiplexing (SDM) fiber transmission systems. On account of the knowledge distillation technologies, the knowledge in large teacher model is transferred to lightweight student model to reduce the complexity of the neural network and the difficulty of deployment. In addition, the adversarial learning is applied to the teacher-student architecture in order to enhance the generalization ability of the student model. After adversarial learning-based knowledge distillation, the student model is suitable for the deployment of the services in optical nodes. Experimentation results indicate that the student model has the 100 % modulation format recognition success rate for QPSK, 8QAM and 16QAM while the RMSE of optical signal-to-noise ratio (OSNR) estimation is below 0.1 dB. Due to its excellent performance and being easy to implement, the proposed scheme has the potential for the next-generation multiple core fiber based optical network.

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Xu Zhu, Bo Liu, Jianxin Ren, Xiaorong Zhu, Yaya Mao, Xiangyu Wu, Mingye Li, Shuaidong Chen, and Yu Bai "Adversarial learning-based knowledge distillation for optical performance monitoring in 7 core fiber", Proc. SPIE 12278, 2021 International Conference on Optical Instruments and Technology: Optical Communication and Optical Signal Processing, 122780M (26 July 2022); https://doi.org/10.1117/12.2619876

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KEYWORDS

Performance modeling

Data modeling

Modulation

Neural networks

Optical networks

Digital signal processing

Fiber couplers

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