Plenary Speech 大会报告


Prof. Tom Hou
IEEE Fellow
Virginia Tech, USA

Tom Hou is the Bradley Distinguished Professor of Electrical and Computer Engineering at Virginia Tech, USA. He received his Ph.D. degree from NYU Tandon School of Engineering (formerly Polytechnic University) in 1998. His current research focuses on developing innovative solutions to complex science and engineering problems arising from wireless and mobile networks. He is particularly interested in exploring new performance limits at the network layer by exploiting advances at the physical layer. In recent years, he has been actively working on real-time optimization based on GPU platform and solving large-scale complex optimization problems for various wireless networks. He is also interested in wireless security. Prof. Hou was named an IEEE Fellow for contributions to modeling and optimization of wireless networks. He has published two textbooks: Cognitive Radio Communications and Networks: Principles and Practices (Academic Press/Elsevier, 2009) and Applied Optimization Methods for Wireless Networks (Cambridge University Press, 2014). Prof. Hou’s research was recognized by five best paper awards from the IEEE and two paper awards from the ACM. He holds five U.S. patents. In addition to his research activities, Prof. Hou has also been active in the research community. He was an Area Editor of IEEE Transaction on Wireless Communications (Wireless Networking area), and an Editor of IEEE Transactions on Mobile Computing, IEEE Journal on Selected Areas in Communications – Cognitive Radio Series, and IEEE Wireless Communications. Currently, he is an Editor of IEEE/ACM Transactions on Networking and ACM Transactions on Sensor Networks. He is the Steering Committee Chair of IEEE INFOCOM conference – the largest and top ranked conference in networking. He was a member of the Board of Governors as well as a Distinguished Lecturer of the IEEE Communications Society.

Speech Title: A New Approach to Real-Time Optimal Scheduling in 5G
Abstract: When addressing optimal scheduling in 5G cellular networks, a key technical challenge is to find an optimal or near-optimal scheduling solution in real-time. Existing schedulers designed for 4G LTE cannot meet the new timing requirements in 5G due to smaller time intervals in 5G numerologies. In this talk we present a GPU-based proportional-fair (PF) scheduler for 5G NR, which can offer near-optimal scheduling solution in real time(e.g., ~100 μs time scale). The key ideas in our solution design include (i) decomposing a large-scale complex optimization problem into a massive number of small and independent sub-problems; (ii) selecting a subset of sub-problems from the most promising search space through intensification and random sampling; and (iii) fitting the selected subset of problems into GPU processing cores for very simple computation. I will share our experience
in this research and show the design of GPF – a GPU-based proportional fair (PF) scheduler that can meet the 100μs time requirement. By implementing our proposed GPF on an off-the-shelf NVIDIA Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the 100 μs time requirement. Our design experience shows that a GPU-based parallel computing holds the potential to design optimal schedulers for current and future generations of telecommunication systems.

Prof. Perry Shum
OSA Fellow, SPIE Fellow
Nanyang Technological University, Singapore

Prof Shum received his PhD degree in Electronic and Electrical Engineering from the University of Birmingham, UK, in 1995. In 1999, he joined the School of Electrical and Electronic Engineering, NTU. Since 2014, he has been appointed as the Director of Centre for Optical Fibre Technology and was the chair, committee member and international advisor of many international conferences. He was also the founding member of IEEE Photonics Society Singapore Chapter (formerly IEEE LEOS). He is currently the chairman of OSA Singapore Chapter. Prof Shum has published more than 500 journal and conference papers with his research interests being in the areas of speciality fibres and fibre-based devices. His H-index is 40. In recent few years, his publications have been cited about 500-800 times per year. He is SPIE Fellow and OSA Fellow.

Speech Title: Optical Fiber Technologies
Abstract: Optical fiber-based devices have been widely deployed in recent years. There are many advantages of using fiber as a sensor. These include electrically-passive operation, light weight, immunity to radio frequency interference and electromagnetic interference, high sensitivity, compact size, corrosion resistance, easily multiplexing and potentially low cost. Several novel fiber-based sensors and technologies developed are presented here, including fiber Bragg grating (FBG) based sensors, photonic crystal fiber (PCF) based sensors, specialty fiber-based sensors and distributed fiber sensing systems. FBGs as instinctive sensors, are ingeniously designed as two-dimensional (2D) tilt sensors, displacement sensors, accelerometers and corrosion sensors here; PCF based evanescent field absorption sensor, PCF induced Mach-Zehnder interferometer and Fabry-Perot refractometer for temperature and refractive index sensing are presented; based on localized surface Plasmon resonant (LSPR) effect, nano-sized fiber tip with gold nanoparticles are demonstrated for live cell index bio-sensing applications.

Prof. Yan Zhang
IEEE Fellow, Fellow of NTVA and IET Fellow
University of Oslo, Norway

Yan Zhang is a Full Professor at the Department of Informatics, University of Oslo, Norway. He received a Ph.D. degree in School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore. His current research interests include: next-generation wireless networks leading to 6G, green and secure cyber-physical systems (e.g., transport, smart grid, and healthcare). His works in these areas have received more than 15000+ citations and H-index 65. He is an editor of 10 IEEE Transactions/Magazines: IEEE Communications Magazine; IEEE Network Magazine; IEEE Communications Surveys & Tutorials;IEEE Transactions on Industrial Informatics; IEEE Transactions on Vehicular Technology; IEEE Transactions on Green Communications and Networking; IEEE Internet of Things Journal;IEEE Systems Journal; IEEE Vehicular Technology Magazine and IEEE Blockchain Technical Briefs. He is IEEE VTS (Vehicular Technology Society) Distinguished Lecturer and CCF 2019 Distinguished Speaker. He serves as the Chair of IEEE ComSoc TCGCC (Technical Committee on Green Communications & Computing). He is an elected member of CCF Technical Committee on Blockchain. He received the award “Highly Cited Researcher” according to Clarivate Analytics in 2019 and 2018. He is IEEE Fellow, Fellow of NTVA, and IET Fellow.

Topic: Edge Intelligence for Internet of Things
Abstract: In this talk, we will first present the key concepts and main principles related to edge intelligence, i.e., the synergy between edge computing and AI. Then, we mainly focus on deep reinforcement learning for addressing key challenges in 5G beyond networks and Internet of Vehicles. In such contexts, we will present our recent studies and results related to content distribution and caching, resource management & optimization, and task offloading problems. We will also point out several open research questions for further study.