Seminar: Self-Adaptive Wireless Systems Through Real-Time Deep Spectrum Learning

 

  

Francesco Restuccia, Ph.D.

Associate Research Scientist, Electrical and Computer Engineering, Northeastern University

 

Friday January 31, 2020

3 pm

Patrick F. Taylor Hall, Room 3107

Abstract

The massive scale and strict performance requirements of next-generation IoT networks will require embedded wireless devices to perform real-time fine-grained optimization of their spectrum usage. Yet, today's networking protocols and architectures are deeply rooted in inflexible designs, and utilize optimization models and strategies that are either too complex or too oversimplified to be fully effective in today's crowded spectrum environment.

In this talk, we are going to introduce and discuss our recent research toward the design of self-adaptive learning-based wireless systems, where transmitters and receivers use real-time deep learning to infer and optimize their networking parameters based on ongoing spectrum conditions. We will conclude the talk by discussing existing challenges and future research directions in the field of real-time deep spectrum learning.

Bio

Francesco Restuccia is currently an Associate Research Scientist with the Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA. His research interests lie in the modeling, analysis, and experimental evaluation of wireless networked systems, with applications to pervasive computing and the Internet of Things. Dr. Restuccia has published over 25 papers in venues such as INFOCOM, MobiHoc and Sensys, as well as co-authoring 8 pending US patents and 2 book chapters. He regularly serves as a TPC Member and reviewer for several ACM and IEEE conferences and journals. Dr. Restuccia is the recipient of the inaugural 2019 Mario Gerla Award for Young Investigators in Computer Science by the Italian Scientists and Scholars of North America Foundation (ISSNAF). He is a Member of the IEEE and the ACM.