Guillem Reus Muns’ PhD Dissertation Defense

“AI for communication and sensing in RF environments”

Committee Members:
Prof. Kaushik Chowdhury (Advisor)
Prof. Stratis Ioannidis
Prof. Hanumant Singh

Abstract:
The recent growth of Internet of Things (IoT), as well as other new
revolutionary applications utilizing wireless spectrum are leading the way towards the realization of next-generation wireless systems that jointly utilize communications and sensing. However, such systems offer many degrees of freedom, and optimizing them for a specific task is difficult to accomplish with deterministic and classical approaches. For this reason, data-driven and AI-based methods have been pursued actively by the research community, as they are able to find solutions that often come close to or exceed the performance of the deterministic counterparts with fractional design complexity. This thesis presents, through real systems and with experimental validation, our progressive efforts in four broad areas, where AI enables the operation of aerial and terrestrial systems that combine sensing and communications. The following key use cases with distinct contributions are investigated:

i) Sensing-aided communications for air and ground systems. First, we present a UAV communication method that defines constellation points in space that map to transmitter frequency bands and are detected at the Base Station using millimeter wave sensors. Second, we explore alternative vehicle-to-infrastructure mmWave beamforming methods, leveraging a) vehicle position and velocity estimation using in-band standard compliant 802.11ad radar and b) camera images and GPS location information.

ii) Signal classification using communication signals, where we propose a) a UAV classification method using uniquely UAV-transmitted signals and b) an RF fingerprinting technique that improves class separation by combining triplet loss with regular classification techniques.

iii) ‘SenseORAN’, a revolutionary architectural design that aims to reuse the cellular infrastructure for sensing purposes in order to address spectrum access challenges in the CBRS band. This is enabled by Open Radio Access Network (O-RAN), a cellular architecture concept that promotes virtualized RANs where disaggregated components are connected via open interfaces and supports intelligent controllers running custom logic. iv) ‘AirFC’, an over-the-air computation method that implements fully connected neural networks inference leveraging multi-antenna wireless systems.