A Survey of Polarization Techniques for Multi-Terminal Source and Channel Coding
A survey of polarization techniques for proving multi-user source and channel coding theorems will be given. Among the scenarios considered will be Slepian-Wolf source coding with side-information, Multi-Access Channel, Broadcast Channel.
Bilkent University, Ankara, Turkey
Erdal Arıkan received the B.S. degree from the California Institute of Technology, Pasadena, CA, in 1981, and the S.M. and Ph.D. degrees from the Massachusetts Institute of Technology, Cambridge, MA, in 1982 and 1985, respectively, all in Electrical Engineering. He served as an assistant professor at the University of Illinois at Urbana-Champaign, before joining in Sept. 2017 the Electrical-Electronics Engineering Department of Bilkent University, Ankara, Turkey, where he is presently a Professor. His research interests have been in the theory and applications of error correcting codes. His work on polar coding has been recognized by several awards, including the 2010 IEEE Information Theory Society Best Paper Award, the 2013 IEEE W. R. G. Baker Award, IEEE Turkey Section 2017 Life-Long Achievement Award, and the 2018 IEEE Hamming Medal. Arıkan is a member of the IEEE and an IEEE Fellow.
Understanding Generative Adversarial Networks
Claude Shannon invented information theory to understand the fundamental limits of communication. Since then, it has revolutionized the communication field. The core of information theory is an approach to research based on... (Read More >>)
Claude Shannon invented information theory to understand the fundamental limits of communication. Since then, it has revolutionized the communication field. The core of information theory is an approach to research based on finding the simplest model to study a problem. Although conceived and cultivated in the context of communication, this approach to research has much broader applicability. In this talk, we illustrate this using our recent work on Generative Adversarial Networks (GANs).
GANs is a novel approach to the age-old problem of learning a probabilistic model from data. Learning is achieved by setting up a game between a generator whose goal is to generate fake data that are close to the real data and a discriminator whose goal is to distinguish between the real and fake data. Even though many increasingly complex GANs architectures have been proposed recently, several basic issues remain unanswered: 1) what is a general way of specifying the loss function of GANs? 2) what is the limiting solution of a GAN as the amount of data increases? 3) what is the generalization ability of a GAN? 4) what is the stability of training GANs under gradient descent? We answer these questions in the simplest setting of the problem. In the process, a connection is drawn between GANs, optimal transport theory, rate-distortion theory and game theory.(Read Less >>)
Stanford University, California, USA
David Tse received the B.A.Sc. degree in systems design engineering from University of Waterloo in 1989, and the M.S. and Ph.D. degrees in electrical engineering from Massachusetts Institute of Technology in 1991 and 1994 respectively. From 1994 to 1995, he was a postdoctoral member of technical staff at A.T. & T. Bell Laboratories. From 1995 to 2014, he was on the faculty of the University of California at Berkeley. He is currently the Thomas Kailath and Guanghan Xu Professor at Stanford University.
David Tse is the recipient of the 2017 Claude E. Shannon Award. Previously, he received a NSF CAREER award in 1998, the Erlang Prize from the INFORMS Applied Probability Society in 2000 and a Gilbreth Lectureship from the National Academy of Engineering in 2012. He received multiple best paper awards, including the Information Theory Society Paper Award in 2003, the IEEE Communications Society and Information Theory Society Joint Paper Awards in 2000, 2013 and 2015, the Signal Processing Society Best Paper Award in 2012 and the IEEE Communications Society Stephen O. Rice Prize in 2013. For his contributions to education, he received the Outstanding Teaching Award from the Department of Electrical Engineering and Computer Sciences at U.C. Berkeley in 2008 and the Frederick Emmons Terman Award from the American Society for Engineering Education in 2009. He is a coauthor, with Pramod Viswanath, of the text Fundamentals of Wireless Communication, which has been used in over 60 institutions around the world. He is the inventor of the proportional-fair scheduling algorithm used in all third and fourth-generation cellular systems.
Information Diagrams for Markov Random Fields
Information diagrams display the set-theoretic structure of Shannon’s information measures. They are very useful for studying information inequalities and identities, especially when certain Markov constraints are imposed on the underlying random variables... (Read More >>)
Information diagrams display the set-theoretic structure of Shannon’s information measures. They are very useful for studying information inequalities and identities, especially when certain Markov constraints are imposed on the underlying random variables. In the past, we knew how to construct information diagrams for Markov chains. In this talk I will present a recursive construction of information diagrams for Markov random fields.(Read Less >>)
Raymond W. Yeung
The Chinese University of Hong Kong, Hong Kong SAR, China
Raymond W. Yeung received his PhD in electrical engineering from Cornell University. He was with AT&T Bell Laboratories from 1988 to 1991. Since 1991, he has been with The Chinese University of Hong Kong, where he is now Choh-Ming Li Professor of Information Engineering and Co-Director of Institute of Network Coding. His research interests include information theory and network coding. He is the author of the textbooks A First Course in Information Theory (Kluwer Academic/Plenum 2002) and its revision Information Theory and Network Coding (Springer 2008), which have been adopted by over 100 institutions around the world. In spring 2014, based on his second book, he gave the first MOOC on information theory on Coursera that reached over 25,000 students.
Dr. Yeung was a member of the Board of Governors of the IEEE Information Theory Society. He was General Chair of the First and the Fourth Workshops on Network, Coding, and Applications (NetCod 2005, 2008), a Technical Co-Chair for the 2006 IEEE International Symposium on Information Theory, a Technical Co-Chair for the 2006 IEEE Information Theory Workshop, and a General Co-Chair of the 2015 IEEE International Symposium on Information Theory. He currently serves as an Editor-at-Large of Communications in Information and Systems, an Editor of Foundation and Trends in Communications and Information Theory and of Foundation and Trends in Networking, and was an Associate Editor for Shannon Theory of the IEEE Transactions on Information Theory.
He was a recipient of the Croucher Foundation Senior Research Fellowship for 2000/2001, the Best Paper Award (Communication Theory) of the 2004 International Conference on Communications, Circuits and System, the 2005 IEEE Information Theory Society Paper Award, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation in 2007, and the 2016 IEEE Eric E. Sumner Award. In 2015, he was named an Outstanding Overseas Chinese Information Theorist by the China Information Theory Society. He is a Fellow of the IEEE, Hong Kong Academy of Engineering Sciences, and the Hong Kong Institution of Engineers.
Polar Code for 5G New Radio
5G is designed to meet very diverse service requirements, ranging from extremely high data rate, ultra-low latency, very high reliability and massive machine type of communications with very low date. This 5G grant vision to design a system usable for many... (Read More >>)
5G is designed to meet very diverse service requirements, ranging from extremely high data rate, ultra-low latency, very high reliability and massive machine type of communications with very low date. This 5G grant vision to design a system usable for many applications pose great technology challenges. The need of a new channel coding is a fundamental question to be addressed for 3GPP 5G New Radio (NR) standardization. In this talk, the design criteria and performance requirements for the code selection are discussed first by looking at the error correcting codes for 3G, 4G and 5G wireless standards. Then the talk will focus on the novel designs of NR adopted Polar code in the following areas: (1) Polar Code with universal rate matching schemes, (2) Polar Code optimization for very short code block sizes, (3) Polar code with extreme high speed decoder (4) Polar Code for blind rate detection (5) Polar Code for Hybrid ARQ, In addition, we also present the state-of-art silicon implementation for polar decoder and field trial results for the superior performance for Polar Code in the real world applications.(Read Less >>)
Huawei Technologies Co., Ltd., Canada
Dr. Peiying Zhu is an IEEE Fellow and Huawei Fellow. She is currently leading 5G wireless system research in Huawei. The focus of her research is advanced wireless access technologies with more than 200 granted patents. She has been regularly giving talks and panel discussions on 5G vision and enabling technologies. She served as the guest editor for IEEE Signal processing magazine special issue on the 5G revolution and IEEE JSAC on Deployment Issues and Performance Challenges for 5G. She co-chaired various 5G workshops in IEEE GLOBECOM. She is actively involved in 3GPP and IEEE 802 standards development. She is currently a WiFi Alliance Board member.
Prior to joining Huawei in 2009, Peiying was a Nortel Fellow and Director of Advanced Wireless Access Technology in the Nortel Wireless Technology Lab. She led the team and pioneered research and prototyping on MIMO-OFDM and Multi-hop relay. Many of these technologies developed by the team have been adopted into LTE standards and 4G products. Peiying Zhu received the Master of Science degree and Doctor Degree from Southeast University and Concordia University in 1985 and 1993 respectively.