Keynote Speaker 2

Prof. Dr. Soon Xin Ng (Michael)

Prof. Dr. Soon Xin Ng (Michael) received the B.Eng. degree (First class) in electronic engineering and the Ph.D. degree in telecommunications from the University of Southampton, Southampton, U.K., in 1999 and 2002, respectively. From 2003 to 2006, he was a postdoctoral research fellow working on collaborative European research projects known as SCOUT, NEWCOM and PHOENIX. Since August 2006, he has been a member of academic staff in the School of Electronics and Computer Science, University of Southampton. He was involved in the OPTIMIX and CONCERTO European projects as well as the IU-ATC and UC4G projects. He was the principal investigator of an EPSRC project on “Cooperative Classical and Quantum Communications Systems“. He is a Senior Member of the IEEE, a Fellow of the Higher Education Academy in the UK, a Chartered Engineer and a Fellow of the IET. He serves as an editor of Quantum Engineering. He was a guest editor for the special issues in IEEE Journal on Selected Areas in Communication as well as editors in the IEEE Access and the KSII Transactions on Internet and Information Systems. He is one of the Founders and Officers of the IEEE Quantum Communications & Information Technology Emerging Technical Subcommittee (QCIT-ETC).

His research interests include various areas in wireless communication explicitly adaptive coded modulation, coded modulation, channel coding, space-time coding, joint source and channel coding, iterative detection, OFDM, MIMO, cooperative communications, distributed coding, quantum communications, quantum error correction codes, joint wireless-and-optical-fibre communications, game theory, artificial intelligence and machine learning. He has published over 260 papers and co-authored two John Wiley/IEEE Press books in this field.

Abstract:

In this contribution, we will investigate how cooperative communications using relay nodes can achieve a higher channel capacity, compared to conventional transmissions. Then, a distributed coding scheme is designed for approaching the corresponding channel capacity. More specifically, a virtual Irregular Convolutional Code (IRCC) is designed based on an iterative learning algorithm and the resultant component encoders are distributed to multiple relay nodes. The near-capacity scheme is applied to an Unmanned Aerial Vehicle (UAV) network for improving the transmission rate at the cell-edge or isolated area. Machine learning algorithm is used to find the optimal location for the UAVs, which serve as the relay nodes. It is shown that a high performing next-generation wireless communications scheme can be created by incorporating cooperative communications, distributed coding and machine learning algorithms.