Seminar Series Archive

The massive scale and strict performance requirements of next-generation wireless networks will require embedded 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 data-driven self-adaptive spectrum-aware 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 technical challenges and possible research directions.

Francesco Restuccia is an Assistant Professor with the Department of Electrical and Computer Engineering, Northeastern University, USA. His research interests lie at the intersection of artificial intelligence, wireless networking, and embedded systems. Dr. Restuccia has published over 50 papers in top-tier networking venues such as INFOCOM, MobiHoc and SenSys, as well as co-authoring 14 pending US patents and 2 book chapters. Dr. Restuccia's research has been funded by the National Science Foundation (NSF) and the Air Force Research Laboratory (AFRL), and has been recognized with the 2019 Mario Gerla Award for Young Investigators in Computer Science by the Italian Scientists and Scholars of North America Foundation (ISSNAF). He is a Senior Member of the IEEE and a Member of the ACM.
Homepage: https://restuccialab.org/