Weiyang Mo is a former research intern in the Optical Networking & Sensing department of NEC Laboratories America, Inc. while studying at University of Arizona.

Posts

Model transfer of QoT prediction in optical networks based on artificial neural networks

An artificial neural network (ANN) based transfer learning model is built for quality of transmission (QoT) prediction in optical systems feasible with different modulation formats. Knowledge learned from one optical system can be transferred to a similar optical system by adjusting weights in ANN hidden layers with a few additional training samples, where highly related information from both systems is integrated and redundant information is discarded. Homogeneous and heterogeneous ANN structures are implemented to achieve accurate Q-factor-based QoT prediction with low root-mean-square error. The transfer learning accuracy under different modulation formats, transmission distances, and fiber types is evaluated. Using transfer learning, the number of retraining samples is reduced from 1000 to as low as 20, and the training time is reduced by up to four times.

Intelligent Filtering-Penalty Monitoring and Mitigation for Cascaded WSSs using Ensemble Learning Algorithm

An ensemble learning algorithm is applied to enhance filtering tolerance of cascaded WSSs in open ROADM environment to demonstrate ~0.8dB Q-factor improvement over MLSE after transmitting over 3200km with 16 ROADMs.

ANN-Based Transfer Learning for QoT Prediction in Real-Time Mixed Line-Rate Systems

Quality of transmission prediction for real-time mixed line-rate systems is realized using artificial neural network based transfer learning with SDN orchestrating. 0.42 dB accuracy is achieved with a 1000 to 20 reduction in training samples.