Invited Talks

 

Yanyong Zhang Professor Rutgers University

 

Yanyong Zhang is currently a Professor in the Electrical and Computer Engineering Department at Rutgers University.  She is also a member of the Wireless Information Networks Laboratory (Winlab). She has 18 years of research experience in the areas of sensor networks, mobile computing and high-performance computing, and has published more than 90 technical papers in these fields. Her current research interests are in future Internet and pervasive computing. Her research is mainly funded by the National Science Foundation, including an NSF CAREER award.

 

Title: MobilityFirst Architecture Considerations for 5G

Speaker: Yanyong Zhang

Abstract:

This talk presents an overview of 5G considerations behind the design of the MobilityFirst future Internet architecture. The MobilityFirst architecture is motivated by a historic shift of the Internet from the fixed host-server model to one in which access from mobile platforms becomes the norm. This implies the need for a future Internet protocol stack designed to handle the special needs of mobility services efficiently and at large scale. A number of key 5G requirements, including user/network mobility, varying wireless link quality and disconnection, multi-homing, ad hoc networking, flexible autonomous system boundaries, and spectrum coordination are identified along with a brief discussion of their implications for protocol design. This is followed by a summary of the MobilityFirst protocol design based on separation of names and locators, global name resolution service, storage-aware routing with hop-by-hop transport, integrated spectrum management, along with an edge-aware inter-domain routing framework. Illustrative examples showing how the MobilityFirst protocol stack supports mobility, multi-homing, and inter-network spectrum coordination are also given.

 

 

Tony Q.S. Quek Associate Professor Singapore University of Technology and Design

 

Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, Tokyo, Japan, respectively. At Massachusetts Institute of Technology (MIT), Cambridge, MA, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is a tenured Associate Professor with the Singapore University of Technology and Design (SUTD). He also serves as the deputy director of the SUTD-ZJU IDEA and a Scientist with the Institute for Infocomm Research. His current research topics include heterogeneous networks, smart grid, green communications, wireless security, big data processing, IoT, and cognitive radio.

Dr. Quek has been actively involved in organizing and chairing sessions, and has served as a TPC member in a numerous international conferences. He is serving as the Workshop Chair for IEEE Globecom in 2017. He is currently an Executive Editorial Committee Member for the IEEE Transactions on Wireless Communications and an Editor for the IEEE Transactions on Communications. He was Guest Editor for the IEEE Signal Processing Magazine (Special Issue on Signal Processing for the 5G Revolution) in November 2014 and the IEEE Wireless Communications Magazine (Special Issue on Heterogeneous Cloud Radio Access Networks) in June 2015. He is a co-author of the book “Small Cell Networks: Deployment, PHY Techniques, and Resource Allocation” published by Cambridge University Press in 2013 and the book “Cloud Radio Access Networks: Principles, Technologies, and Applications” by Cambridge University Press in 2016.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the IEEE Globecom 2010 Best Paper Award, the 2012 IEEE William R. Bennett Prize, the IEEE SPAWC 2013 Best Student Paper Award, the IEEE WCSP 2014 Best Paper Award, the IEEE PES General Meeting 2015 Best Paper, and the 2015 SUTD Outstanding Education Awards – Excellence in Research.

 

Title: Cell-Edge-Aware Precoding for Downlink Massive MIMO Cellular Networks

Speaker: Tony Q. S. Quek

Abstract:

We propose a cell-edge-aware (CEA) zero forcing (ZF) precoder that exploits the excess spatial degrees of freedom provided by a large number of base station (BS) antennas to suppress inter-cell interference at the most vulnerable user equipments (UEs). We evaluate the downlink performance of CEA-ZF, as well as that of a conventional cell-edge-unaware (CEU) ZF precoder in a network with random base station topology. Our analysis and simulations show that the proposed CEA-ZF precoder outperforms CEU-ZF precoding in terms of (i) aggregate per-cell data rate, (ii) coverage probability, and (iii) 95%-likely, or edge user, rate. This result identifies CEA-ZF as a more effective precoding scheme for massive MIMO cellular networks. Our framework also reveals the importance of scheduling the optimal number of UEs per BS, and confirms the necessity to control the amount of pilot contamination received during the channel estimation phase.

 

 

Wee Peng Tay Assistant Professor Nanyang Technological University, Singapore

 

Wee Peng Tay received the B.S. degree in Electrical Engineering and Mathematics, and the M.S. degree in Electrical Engineering from Stanford University, Stanford, CA, USA, in 2002. He received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2008. He is currently an Assistant Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. He is an Associate Editor for the IEEE Transactions on Signal Processing, serves on the MLSP TC of the IEEE Signal Processing Society, and is the chair of DSNIG in IEEE MMTC. He has also served as a technical program committee member for various international conferences. His research interests include distributed detection and estimation, distributed signal processing, sensor networks, social networks, information theory, and applied probability.

 

Title: Towards Information Privacy for the Internet of Things

Speaker: Wee Peng Tay

Abstract:

In an Internet of Things network, multiple sensors send information to a fusion center for it to infer about a public hypothesis of interest. However, the same sensor information may be used by the fusion center to make inferences of a private nature that the sensors wish to protect. To model this, we adopt a decentralized hypothesis testing framework with binary public and private hypotheses. Each sensor makes a private observation and utilizes a local sensor decision rule to summarize that observation before sending to the fusion center. We adopt a nonparametric approach to design local sensor decision rules that allow the fusion center to detect a public hypothesis with minimal regularized empirical risk,

while keeping the empirical risk of detecting the private hypothesis above a privacy threshold. We develop iterative optimization algorithms to determine an appropriate privacy threshold and the best sensor local decision rules, and show that they converge. Numerical results on both synthetic and real data sets suggest that our proposed approach yields low error rates for inferring the public hypothesis, but high error rates for detecting the private hypothesis.