Séminaire ICI : Lorenzo Miretti
Titre du séminaire et orateur
Dealing With Noisy Information in Cooperative Multi-Agent Networks: Enforcing Common Information.
Lorenzo Miretti (EURECOM)
Date et lieu
Mardi 11 juin 2019, 11h
ENSEA, salle 384
Decentralized decisional networks are composed of agents that take coordinated decisions on the basis of individual noisy informations about the system state, i.e. under a so-called distributed state information configuration. The general category of problems involved in such systems, namely team decision problems, is known to be NP-hard, leading to the need for efficient sub-optimal solutions. The application of algorithms directly derived from classical centralized optimization often incurs severe performance degradation, mostly due to the lack of coordination between the agents. Motivated by the above intuition, we discuss in this talk how to transform a general team decision problem into a more treatable form with a higher degree of coordination, at the expense of local state knwoledge degradation. In particular, we formulate and characterize the fundamental trade-off lying behind this operation, coined here as the distortion-predictability trade-off. Furthermore, results on cooperative wireless communication networks are presented both for motivation and as examples of successful application of the proposed idea.
Lorenzo Miretti received the BSc and MSc degrees in Telecommunication Engineering from Politecnico di Torino in 2015 and 2018 respectively, both cum laude. From July 2017 to December 2017, he was a student researcher at Fraunhofer HHI Institute, Berlin, where he authored several publications and a patent on the topic of channel acquisition for Massive MIMO systems. In February 2018 he joined as a PhD student the Communication Systems Department of EURECOM, Sophia Antipolis, working on new paradigms for mobile networks based on recasting devices as distributed computational nodes, under the supervision of David Gesbert. His current research interests lie in the area of distributed optimization for communication networks, multi-antenna and multi-agent signal processing, and multi-user information theory.