Toward Secure, Resilient, and AI-Driven Machine-to-Machine Wireless Communications

George Sklivanitis, Ph.D.
Research Associate Professor
Florida Atlantic University
Boca Raton, FL

Date:
02/16/2024

Time:
11:00 am – 12:00 pm CST

Location:
San Pedro 1, Yotta Room 430
506 Dolorosa St, San Antonio, TX 78204

Zoom: https://utsa.zoom.us/j/94807623288

Abstract:
Next-generation (NextG) network systems such as 6G cellular, future versions of WiFi, satellite networks are expected to connect billions of heterogeneous Internet of Thing (IoT) devices along with billions of people, enable machine-to-machine communications, and provide low-latency computational and storage resources on-demand at the edge and in the cloud. Any failure, tampering or degradation in network service can have highly disruptive, if not potentially catastrophic, effects. NextG network systems should be secure, intelligent and support autonomous decision-making. In this talk, I will present our work on model-based algorithms for waveform design for NextG wireless systems that autonomously optimize how to exchange information with any desirable level of interference avoidance allowed by the physics of the medium and hardware limitations. I will explore challenges and opportunities associated with AI-driven spectrum sensing architectures that provide the support for NextG waveform design and optimization. I will also cover data-driven frameworks enabling NextG security at the physical layer with functionalities such as radio fingerprinting for device authentication and dynamic link encryption using wireless channel state information. I will present our implementation work and experiments with software-defined radio platforms and discuss the way forward.