Plenary: Short Packets over Wireless Fading Networks

Session chair(s):
Vincent
Y. F.
Tan

Presenter

Profile picture for user GiuseppeDurisi
Giuseppe
Durisi
Chalmers University of Technology, Sweden

Abstract

To support the Internet-of-Things vision of enabling distributed autonomous systems operating in real time, we need a new wireless infrastructure, able to provide highly reliable and low-latency connectivity to a large number of sporadically active devices transmitting short data packets. In this talk, I will illustrate how to use recent results from finite-blocklength information theory to optimally design such a wireless infrastructure. Scenarios that are relevant for 5G and beyond will be presented. In particular, I will discuss how to support low-latency, ultra-reliable communications in both cellular and cell-free massive multiple-input multiple-output architectures.

Biography

Giuseppe Durisi
Giuseppe Durisi received the Laurea degree summa cum laude and the Doctor degree both from Politecnico di Torino, Italy, in 2001 and 2006, respectively. From 2002 to 2006, he was with Istituto Superiore Mario Boella, Torino, Italy. From 2006 to 2010 he was a postdoctoral researcher at ETH Zurich, Zurich, Switzerland. In 2010, he joined Chalmers University of Technology, Gothenburg, Sweden, where he is now full professor with the Communication Systems Group. Dr. Durisi is a senior member of the IEEE. He is the recipient of the 2013 IEEE ComSoc Best Young Researcher Award for the Europe, Middle East, and Africa Region, and is co-author of a paper that won a “student paper award” at the 2012 International Symposium on Information Theory, and of a paper that won the 2013 IEEE Sweden VT-COM-IT joint chapter best student conference paper award. In 2015, he joined the editorial board of the IEEE Transactions on Communications as associate editor. From 2011 to 2014, he served as publications editor for the IEEE Transactions on Information Theory. His research interests are in the areas of communication and information theory and machine learning.