Service Provisioning in 5G Communication Networks
FIEEE, FRSC, FCAE, FEIC, PEng Tier I Canada Research Chair
Broadband Communications Research (BBCR) Group
Department of Electrical and Computer Engineering
University of Waterloo, Canada
Abstract: The fifth generation (5G) communication networks will accommodate a wide range of emerging services with diverse service quality requirements. The network will integrate a variety of network resources and technologies to support high transmission rate and to enhance quality of experience to mobile users. The traditional one-size-fits-all network architecture cannot efficiently meet the needs of different services, due to the poor scalability, limited adaptability, and inflexibility. Network function virtualization (NFV) enabled by software defined networking (SDN) technology is a promising approach for an agile and flexible 5G networking infrastructure. In this presentation, we will provide an overview of several recent studies for 5G networks, including dynamic radio resource slicing in wireless network virtualization, computing and transmission resource allocation in the core network, and how to establish a customized virtual network topology for multicast services.We will conclude this presentation with a brief discussion of some open research topics.
Dr. Weihua Zhuang has been with the
Department of Electrical and Computer Engineering, University of
Waterloo, Canada, since 1993, where she is a Professor and a Tier I
Canada Research Chair in Wireless Communication Networks. She is the
recipient of 2017 Technical Recognition Award from IEEE Communications
Society Ad Hoc & Sensor Networks Technical Committee, one of 2017 ten
N2Women (Stars in Computer Networking and Communications), and a
co-recipient of several best paper awards from IEEE conferences. Dr.
Zhuang was the Editor-in-Chief of IEEE Transactions on Vehicular
Technology (2007-2013), Technical Program Chair/Co-Chair of IEEE VTC
Fall 2017 and Fall 2016, and the Technical Program Symposia Chair of the
IEEE Globecom 2011. She is a Fellow of the IEEE, the Royal Society of
Canada, the Canadian Academy of Engineering, and the Engineering
Institute of Canada. Dr. Zhuang is an elected member in the Board of
Governors and VP Publications of the IEEE Vehicular Technology Society.
Spatial Deep Learning for Wireless Scheduling
PEng Tier I
Canada Research Chair
Electrical and Computer Engineering Department
University of Toronto, Canada
Abstract: The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. In this talk, we first propose a novel fractional programming method to solve this problem, then point out that the traditional optimization approach of first estimating all the interfering channel strengths then optimizing the scheduling based on the model is not always practical, because channel estimation is resource intensive, especially in dense networks. To address this issue, we investigate the possibility of using a deep learning approach to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers. This can be accomplished both by supervised learning using locally optimal schedules generated from fractional programming for randomly deployed device-to-device networks as training data and by unsupervised learning. In both cases, we use a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives good performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Further, we propose a novel approach of utilizing the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links to provide fair scheduling across the network, thereby showing the promise of using deep learning to solve discrete optimization problems in wireless networking.
Wei Yu received the B.A.Sc. degree in Computer Engineering and
Mathematics from the University of Waterloo, Waterloo, Ontario, Canada
in 1997 and M.S. and Ph.D. degrees in Electrical Engineering from
Stanford University, Stanford, CA, in 1998 and 2002, respectively. He is
now Professor and holds a Canada Research Chair (Tier 1) in Information
Theory and Wireless Communications in the Electrical and Computer
Engineering Department at the University of Toronto, Canada. Prof. Wei
Yu currently serves on the IEEE Information Theory Society Board of
Governors. He was an IEEE Communications Society Distinguished Lecturer
(2015-16), and currently serves as an Area Editor for the IEEE
Transactions on Wireless Communications. He is currently the Chair of
the Signal Processing for Communications and Networking Technical
Committee of the IEEE Signal Processing Society. Prof. Wei Yu received
the IEEE Signal Processing Society Best Paper Award in 2017 and 2008,
the Journal of Communications and Networks Best Paper Award in 2017, an
E.W.R. Steacie Memorial Fellowship in 2015, and an IEEE Communications
Society Best Tutorial Paper Award in 2015. Prof. Wei Yu is recognized as
a Highly Cited Researcher. He is a Fellow of IEEE and a Fellow of
Canadian Academy of Engineering.
Deep Learning in Physical Layer Communications
Geoffrey Ye Li
School of Electrical and Computer Engineering
Georgia Institute of Technology, USA
Abstract: It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of communication systems. In this talk, we introduce our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) module in communication systems or optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection, including some experimental results, and discuss model-driven DL in communication systems. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems. At the end of the talk, we provide some potential research topics in the area.
Biography: Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by over 33,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.