Antonia María Tulino

Antonia María Tulino-Universitá Degli Studi Di Napoli, Federico II. Italy

Antonia M. Tulino is full professor at DIETI of the Universitá di Napoli Federico II. Since 2009, she collaborates with the Math of Communication Dep. in Bell Labs. Prof. Tulino has contributed extensively to the information theoretic understanding of the potential and ultimate limitations of MIMO systems. Her research interests lay in the broad area of communication systems approached with the complementary tools provided by signal processing, information theory and random matrix theory.  Her work has received multiple awards, including the IEEE Communication Society Stephen O. Rice Paper Award in 2009. Overall, she has published 1 book, 6 book chapters, 55 journal papers, 130 papers in international conferences, and 26 patents (14 Granted, 12 filled). Dr. Tulino is a Fellow of the IEEE for contributions to the development and application of random matrix methods in information theory and she was selected by the National Academy of Engineering for the Frontiers of Engineering program in 2013.

Research stay at UC3M: DEPARTMENT OF SIGNAL THEORY AND COMMUNICATIONS

Project:

Compressed Sensing for next generation wIreless NEtworks  (COSINE)

Mobile and wireless communications will increasingly become the primary way for humans and machines to access information and services of any kind, from traditional communication services, to industrial automation, smart cities, and augmented reality, among others. To this end, the next generation of mobile communication networks is expected to provide several orders of magnitude improvements in overall wireless system capacity. One of the main enablers for this to happen is the Dynamic Radio Access Network (DyRAN), which provides a Radio Access Network (RAN) that adapts to rapid spatio-temporal changes in user demands for the mix of emerging real-time/personalized applications.  The objective of this project is, starting from some of the key radio access technologies that DyRAN incorporates, is to identify the corresponding research challenges, and discuss the cornerstone role that compressed sensing (CS) can play in the efficient evolution towards flexible and adaptive wireless networks.