Shuangyi Yan is currently a lecturer in High Performance Networking & Optical Networking at the University of Bristol. He received the B.E degree in information engineering from Tianjin University, Tianjin, China in 2004. In 2009, he got the PhD degree in optical engineering from Xi’an Institute of Optics and Precision Mechanics, CAS, Xi’an, China. His doctoral dissertation focused on key technologies in ultra-high-speed optical communication networks, such as ultra-short optical pulse generation, high-bandwidth optical signal processing. From 2011 to 2013, He worked on the spectra-efficient long-haul transmission system and low-cost short-range transmission system in Photonics Research Centre, Dept. EIE of the Hong Kong Polytechnic University, Hong Kong. He was involved in several industrial funded projects. In July 2013, he joined the High Performance Networks Group at University of Bristol. His research interests include Artificial intelligence in Optical Networks, multi-dimensional programmable optical networks, multi-layer network analytics for network optimisation, and next generation data centre networks. He is the author or co-author of over 60 publications, of which consist patents and several post deadline papers in optical communication related top-level conferences.
Artificial Intelligence in Optical Networks, Automatic programmable networks, 5G bearer networks
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.
We adopted K-means clustering to efficiently partition the subcarriers to reduce the complexity of PS-QAM on FBMC/OQAM system using KK receiver. The net data rate of 100 Gb/s is achieved after 125 km transmission.