Here is a tentative schedule of tutorials:
To accommodate the ever-increasing mobile data traffic and support enhanced mobile broadband services, there is a tremendous demand in boosting the capacity of wireless networks. One promising way is to exploit the spatial domain resources by deploying more antennas at transceivers, especially at the base station. Another effective approach is via network densification, which can significantly improve the area spectral efficiency. Therefore, multi-antenna networking forms a foundation for next-generation wireless networks. It is thus of significant practical importance to understand the performance of such complicated networks. While simulations can demonstrate many superior features of multi-antenna networks, mathematical analysis is essential to help expose their salient properties and provide effective design mechanisms without building and running complex system-level simulations. The aim of this tutorial is to present a unified analytical framework for tractable analysis of large-scale multi-antenna wireless networks via stochastic geometry. Compared with existing methods, this framework leads to more tractable expressions and makes the analysis of multi-antenna networks comparable to that of the single-antenna counterpart. Various application examples with different multi-antenna network models will be presented to illustrate the effectiveness of this analytical framework.
Dr. Jun Zhang received the Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin in 2009. He is currently a Research Assistant Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST). His current research interests include analysis and optimization of dense wireless cooperative networks, mobile edge computing, cloud computing, and big data analytics systems.
Dr. Zhang co-authored the book "Fundamentals of LTE" (Prentice-Hall, 2010). He is a co-recipient of the 2016 Marconi Prize Paper Award in Wireless Communications, the 2014 Best Paper Award for the EURASIP Journal on Advances in Signal Processing, an IEEE GLOBECOM Best Paper Award in 2017, an IEEE ICC Best Paper Award in 2016, and an IEEE PIMRC Best Paper Award in 2014. One paper he co-authored received the 2016 Young Author Best Paper Award of the IEEE Signal Processing Society. He also received the 2016 IEEE ComSoc Asia-Pacific Best Young Researcher Award. He is an Editor of IEEE Transactions on Wireless Communications, and is a guest editor of the special section on "Mobile Edge Computing for Wireless Networks" in IEEE Access. He frequently serves on the technical program committees of major IEEE conferences in wireless communications, such as ICC, Globecom, WCNC, VTC, etc., and served as a MAC track co-chair for IEEE WCNC 2011.
Advanced persistent threats (APTs) are a major threat to cyber-security, causing significant financial and privacy losses each year. Wireless systems and devices such as smartphones, laptops, and base stations are all vulnerable to APTs, as APT attackers launch multiple types of attack methods to steal information from these systems without being detected over a long period. In this tutorial, we introduce game-theoretic models to understand the behavior of the APT, assess cyber risks, and develop defense mechanism. The tutorial first gives an overview of game theoretic methods for cyber security and then focuses on several classes of static and dynamic game frameworks including Colonel Blotto games, multi-stage dynamic games, signaling games, and large-scale games. The tutorial also discusses prospect theory to model the risk-seeking and risk-aversion behaviors of the players and explain the deviations of their decisions from the expected utility-based results. We will also present computational and learning algorithms that can be implemented to improve the security and resilience of the cyber systems against APTs. The discussions will include the approximate dynamic programming approaches, hotbooting deep Q-network (DQN)-based schemes, and nonparametric statistical tools.
Quanyan Zhu (S'04-M'12) is an assistant professor in the Department of Electrical and Computer Engineering at New York University. He received the B. Eng. in Honors Electrical Engineering with distinction from McGill University in 2006, the M.A.Sc. from University of Toronto in 2008, and the Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) in 2013. From 2013-2014, he was a postdoctoral research associate at the Department of Electrical Engineering, Princeton University. He is a recipient of many awards including NSERC Canada Graduate Scholarship (CGS), Mavis Future Faculty Fellowships, and NSERC Postdoctoral Fellowship (PDF). He spearheaded and chaired INFOCOM Workshop on Communications and Control on Smart Energy Systems (CCSES), Midwest Workshop on Control and Game Theory (WCGT), and 7th Game and Decision Theory for Cyber Security (GameSec). His current research interests include resilient and secure interdependent critical infrastructures, energy systems, cyber-physical systems, and smart cities.
Liang Xiao (M'09, SM'13) is currently a Professor in the Department of Communication Engineering, Xiamen University, Fujian, China. She received the B.S. degree in communication engineering from Nanjing University of Posts and Telecommunications, China, in 2000, the M.S. degree in electrical engineering from Tsinghua University, China, in 2003, and the Ph.D. degree in electrical engineering from Rutgers University, NJ, in 2009. She was a visiting professor with Princeton University, Virginia Tech, and University of Maryland, College Park. She has served as an associate editor of IEEE Trans. Information Forensics and Security and guest editor of IEEE Journal of Selected Topics in Signal Processing. She is the recipient of the best paper award for 2016 INFOCOM Big Security WS and 2017 ICC. Her current research interests include smart grids, network security, and wireless communications.
This tutorial will identify and discuss technical challenges and recent results related to the UDN in 5G mobile networks. The tutorial is mainly divided into four parts. In the first part, we will introduce UDN, discuss about the UDNs system architecture, and provide some main technical challenges. In the second part, we will focus on the issue of resource management in UDN and provide different recent research findings that help us to develop engineering insights. In the third part, we will address the signal processing and PHY layer design of UDN and address some key research problems. In the last part, we will summarize by providing a future outlook of UDN.
Haijun Zhang (M'13, SM'17) is currently a Full Professor in University of Science and Technology Beijing, China. He was a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Vancouver Campus, Canada. From 2011 to 2012, he visited Centre for Telecommunications Research, King's College London, London, UK, as a Visiting Research Associate. He serves as Editor of IEEE Transactions on Communications, IEEE 5G Tech Focus, EURASIP Journal on Wireless Communications and Networking, and Journal of Network and Computer Applications, and serves/served as a Leading Guest Editor for IEEE Communications Magazine, and IEEE Transactions on Emerging Topics in Computing. He serves/served as General Co-Chair of GameNets'16, Symposium Chair of Globecom'19, IWCMC'18, GameNets'14 and ScalCom2015, TPC Co-Chair of INFOCOM 2018 Workshop on Integrating Edge Computing, Caching, and Offloading in Next Generation Networks, General Co-Chair of ICC 2018 (ICC 2017, Globecom 2017) Workshop on 5G Ultra Dense Networks, and General Co-Chair of Globecom 2017 Workshop on LTE-U. He received the IEEE ComSoc Young Author Best Paper Award in 2017.
Networks of caches naturally arise in many networking applications involving the distribution of content, including information centric networks (ICNs), content distribution networks (CDNs), and wireless edge networks, to name a few. Though, e.g., secondary-memory caches are well-studied, in contrast, the study of networks of caches still poses significant challenges. First, there exists an inherent computational challenge: this is precisely because of the combinatorial nature of content placement problems, many of which are NP-hard. Second, closed form solutions of quantities of interest (as, e.g., expected delay, hit/miss rates) become hard to obtain in multi-hop settings: even if arriving traffic in an LRU cache is, e.g., Poisson, the outgoing traffic may be hard to describe analytically. In this tutorial, we review several recently developed frameworks for the optimal design of cache networks under arbitrary multi-hop topologies. Well grounded in the theory of stochastic control, optimization, and distributed systems, these frameworks yield new algorithms for the joint optimization of forwarding, caching, scheduling, and congestion control in cache networks. Most importantly, these frameworks address both the combinatorial challenge related to caching, as well as modeling issues that arise due to the multi-hop nature of cache networks, for arbitrary topologies. We will review approximation algorithms for the corresponding offline problems, that provide optimality guarantees in terms of throughput, delay, and utility performance. The tutorial will also cover distributed, dynamic, algorithms, that are adaptable to changing user demands and network conditions. The tutorial targets both theorists and practitioners, interested in the modeling, analysis, and implementation of cache networks.
Stratis Ioannidis is an Assistant Professor in the Electrical and Computer Engineering Department of Northeastern University, in Boston, MA, where he also holds a courtesy appointment with the College of Computer and Information Science. He received his B.Sc. (2002) in Electrical and Computer Engineering from the National Technical University of Athens, Greece, and his M.Sc. (2004) and Ph.D. (2009) in Computer Science from the University of Toronto, Canada. Prior to joining Northeastern, he was a research scientist at the Technicolor research centers in Paris, France, and Palo Alto, CA, as well as at Yahoo Labs in Sunnyvale, CA. He is the recipient of an NSF CAREER Award, a Google Faculty Research Award, and a best paper award at the 2017 ACM Conference in Information Centric Networks (ICN). He has worked extensively on the design, modeling, and analysis of content distribution and caching algorithms for large-scale networks.
Edmund Yeh is a Professor of Electrical and Computer Engineering at Northeastern University, Boston, USA. He received his B.S. in Electrical Engineering with Distinction and Phi Beta Kappa from Stanford University in 1994. He then studied at Cambridge University on the Winston Churchill Scholarship, obtaining his M.Phil in Engineering in 1995. He received his Ph.D. in Electrical Engineering and Computer Science from MIT under Professor Robert Gallager in 2001. He was previously Assistant and Associate Professor of Electrical Engineering, Computer Science, and Statistics at Yale University. Professor Yeh was one of the co-PIs on the original NSF-funded FIA Named Data Networking project. He is the recipient of the Alexander von Humboldt Research Fellowship, the Army Research Office Young Investigator Award, and the Best Paper Award at the 2015 IEEE International Conference on Communications (ICC) Communication Theory Symposium, London, UK, and at the 2017 ACM Conference on Information Centric Networks (ICN), in Berlin, Germany.