ANN-Based Multi-Channel QoT-Prediction over a 563.4-Km Field-Trial Testbed

In this paper, artificial neural network (ANN)-based multi-channel Q-factor prediction is investigated with real-time network operation and configuration information over a 563.4-km field-trial testbed. A unified ANN-based regression model is proposed and implemented to predict Q-factors of all the channels simultaneously. A scenario generator is developed to configure the field-trial testbed with 8 testing channels automatically to generate dynamic scenarios. A network configuration and monitoring database (CMDB) is implemented to collect network configuration and monitoring data that include link information, operational parameters of key optical devices, network configuration state, and real-time Q-factors of the available channels for the generated network scenarios. These collected data are used for training and testing of the developed ANN model. In order to achieve multiple channel predictions, we propose a hot coding method to represent the state of dynamic channel. Besides, an auto-search method is used to search the best hyperparameters of the ANN-based model. The results show that the proposed ANN-based regression model converges quickly, and it can predict the multi-channel’s Q-factors with high accuracy. The unified ANN-based multi-channel Q-factor regression model can provide the comprehensive information to assist SDN controller to optimize network configuration for dynamic optical networks.