Joint Modulation Format Identification and OSNR Monitoring Using Cascaded Neural Network With Transfer Learning

• Main Authors: Jing Zhang, Yuanjian Li, Shaohua Hu, Wanting Zhang, Zhiquan Wan, Zhenming Yu, Kun Qiu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Photonics Journal
• Subjects: Optical performance monitoring; coherent communication; machine learning; transfer learning
• Online Access: https://ieeexplore.ieee.org/document/9345347/

We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100%. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.