Neural-Network-Based G-OSNR Estimation of Probabilistic-Shaped 144QAM Channels in DWDM Metro Network Field Trial

A two-stage neural network model is applied on captured PS-144QAM raw data to estimate channel G-OSNR in a metro network field trial. We obtained 0.27dB RMSE with first-stage CNN classifier and second-stage ANN regressions.

[1]  Ting Wang,et al.  41.5 Tb/s Data Transport over 549 km of Field Deployed Fiber Using Throughput Optimized Probabilistic-Shaped 144QAM to Support Metro Network Capacity Demands , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[2]  Kohei Nakamura,et al.  Nonlinear Characterization of Fiber Optic Submarine Cables , 2017, 2017 European Conference on Optical Communication (ECOC).

[3]  David J. Ives,et al.  Joint Estimation of Linear and Non-linear Signal-to-Noise Ratio based on Neural Networks , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[4]  Takeshi Hoshida,et al.  Data-analytics-based Optical Performance Monitoring Technique for Optical Transport Networks , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[5]  Daniel C. Kilper,et al.  ANN-Based Transfer Learning for QoT Prediction in Real-Time Mixed Line-Rate Systems , 2018, 2018 Optical Fiber Communications Conference and Exposition (OFC).

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  R. Theodore Hofmeister,et al.  Lessons learned from open line system deployments , 2017, 2017 Optical Fiber Communications Conference and Exhibition (OFC).