Keynotes and invited papers

Abstract: The radio networks' paradigm is gradually shifting from servicing a few simultaneously-active trafic-hungry (human) users to hundreds of thousands of low-rate (machine) users. In this talk we consider new information-theoretic (IT) questions arising from formalizing this problem. A surprising discovery is existence of coded-access schemes that are able to perfectly reject multi-user interference, so that increasing the density of users (without increasing space-time-frequency resources!) does not lead to any deterioration of service. What is perhaps more important is that all of the industry-standard schemes (treating interference as noise, orthogonalization, ALOHA) become severly energy inefficient as user-density increases. I will discuss some of the proposed solutions for solving multiple (and random) access to alleviate these issues. Theoretically, we discover that this transition in IT parallels the one in statistics when dimensionality of feature (regressor) vectors has dramatically increased and sparsity considerations surfaced. Similarly, we will find that traditional IT tools need to be augmented with methods such as Gaussian widths and Gordon's lemma. In fact our methods allow us to derive simple firm bounds that closely match replica-method predictions from statistical physics.

Biography: Yury Polyanskiy is an Associate Professor of Electrical Engineering and Computer Science and a member of LIDS at MIT. Yury received M.S. degree in applied mathematics and physics from the Moscow Institute of Physics and Technology, Moscow, Russia in 2005 and Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2010. Currently, his research focuses on basic questions in information theory, error-correcting codes, wireless communication and fault-tolerant and defect-tolerant circuits. Dr. Polyanskiy won the 2013 NSF CAREER award and 2011 IEEE Information Theory Society Paper Award.

Abstract: In this talk, we introduce a new methodology for modeling and analyzing downlink cellular networks, where the Base Stations (BSs) constitute a motion-invariant Point Process (PP) that exhibits some degree of interactions among the points, i.e.,spatial repulsion or spatial clustering. The proposed approach is based on the theory of Inhomogeneous Poisson PPs (I-PPPs) and is referred to as Inhomogeneous Double Thinning (IDT) approach. In a PP, the distribution of the distance from a randomly distributed (typical) user to its nearest BS depends on the degree of spatial repulsion or clustering exhibited by the PP. Also, the average number of interfering BSs that lie within a given distance from the typical user is a function of the repulsion and clustering characteristics of the PP. The proposed approach consists of approximating the original motion-invariant PP with an equivalent PP that is made of the superposition of two conditionally independent I-PPPs. The inhomogeneities of both PPs are created from the point of view of the typical user (“user-centric”): The first one is based on the distribution of the user’s distance to its nearest BS and the second one is based on the distance-dependent average number of interfering BSs around the user. The inhomogeneities are mathematically modeled through two distance-dependent thinning functions and a tractable expression of the coverage probability is obtained. Sufficient conditions on the parameters of the thinning functions that guarantee better or worse coverage compared with the baseline homogeneous PPP model are identified. The accuracy of the IDT approach is substantiated with the aid of empirical data for the spatial distribution of the BSs. This talk is based on the research article titled “Inhomogeneous Double Thinning - Modeling and Analysis of Cellular Networks by Using Inhomogeneous Poisson Point Processes", and published in IEEE Trans. Wireless Communications 17(8): 5162-5182 (2018).

Biography: Marco DI RENZO was born in L' Aquila, Italy, in 1978. He received the Laurea (cum laude) and Ph.D. degrees in electrical engineering from the University of L' Aquila, Italy, in 2003 and 2007, respectively, and the Habilitation à Diriger des Recherches (Doctor of Science) degree from University Paris-Sud, France, in 2013. Since 2010, he has been a Chargé de Recherche CNRS (CNRS Associate Professor) in the Laboratory of Signals and Systems (L2S) of Paris-Saclay University - CNRS, CentraleSupélec, Univ Paris Sud, Paris, France. He serves as the Associate Editor-in-Chief of IEEE Communications Letters, and as an Editor of IEEE Transactions on Communications, and IEEE Transactions on Wireless Communications. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society and IEEE Communications Society, and a Senior Member of the IEEE. He is a recipient of several awards, including the 2013 IEEE-COMSOC Best Young Researcher Award for Europe, Middle East and Africa, the 2013 NoE-NEWCOM# Best Paper Award, the 2014-2015 Royal Academy of Engineering Distinguished Visiting Fellowship, the 2015 IEEE Jack Neubauer Memorial Best Systems Paper Award, the 2015-2018 CNRS Award for Excellence in Research and Ph.D. Supervision, the 2016 MSCA Global Fellowship (declined), the 2017 SEE-IEEE Alain Glavieux Award “for outstanding results in developing several mathematical abstractions (for mobile network modeling), innovating ideas, and demonstrating their usefulness in future wireless communication systems”, the 2018 IEEE COMSOC Best Young Professional Award in Academia “for outstanding contributions to the academia in terms of innovative Communications Research and IEEE ComSoc Services, especially in the area of emerging physical-layer technologies”, and 7 conference Best Paper Awards (2012 and 2014 IEEE CAMAD, 2013 IEEE VTC-Fall, 2014 IEEE ATC, 2015 IEEE ComManTel, 2017 IEEE SigTelCom, 2018 INISCOM).

Abstract: This talk will present the role of totally positive kernels and Po'lya type distributions to information theory. In particular, it will be discussed how the variational diminishing property of Po'lya type distributions, which is captured by the Oscillation Theorem, can be used to characterize the structure of capacity-achieving distributions for a large class of channels.

Biography: Alex Dytso is currently a Postdoctoral Researcher in the Department of Electrical Engineering at Princeton University working under the supervision of Professor H. Vincent Poor. He received a Ph.D. degree from the Department of Electrical and Computer Engineering at the University of Illinois at Chicago (UIC) under the supervision of Daniela Tuninetti and Natasha Devroye. He received his B. S. degree in 2011 from the University of Illinois at Chicago where he received International Engineering Consortium's William L. Everitt Student Award of Excellence for outstanding seniors, who have demonstrated an interest in the communications field. His current research topic focuses on multi-user information and estimation theories and their applications to wireless networks.

Abstract: Due to massive numbers of uncoordinated devices present in wireless networks for the Internet of Things (IoT), interference is a key challenge. There is evidence both from experiments and analysis of statistical models that the uncoordinated nature of channel access leads to non-Gaussian statistics for the interference. A particularly attractive model in this scenario is the additive vector α-stable noise channel. In this paper, we study the capacity of this channel with fractional moment constraints. In particular, we establish well-posedness of the optimization problem for the capacity. We also study convergence of the capacity loss due to an additional constraint where input probability measures are concentrated on spherical shells, in addition to the fractional moment constraints.

Biography: Malcolm Egan received the Ph.D. in Electrical Engineering in 2014 from the University of Sydney, Australia. He is currently an Assistant Professor in CITI, a joint laboratory between INRIA, INSA Lyon and University of Lyon, France. Previously he was with the Mathematical Laboratory of University Blaise Pascal, France and the Department of Computer Science, Czech Technical University in Prague, Czech Republic. His research interests are in the areas of information theory and statistical signal processing with applications in wireless and molecular communications.