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Daniel Uvaydov’s PhD Proposal Review
December 15, 2022 @ 9:00 am - 11:00 am
“Real-Time Spectrum Sensing for Inference and Control”
Abstract:
Spectrum sensing can enable the next generation of wireless applications ranging from opportunistic spectrum access to cognitive radio networks. The key unaddressed challenges of spectrum sensing are that (i) it has to be performed with extremely low latency over varying bandwidths and must guarantee strict real-time processing constraints; (ii) its underlying algorithms need to be extremely accurate, and flexible enough to work with different wireless bands and protocols to find application in real-world settings. We address these challenges in multiple wireless applications by utilizing Deep Learning techniques as the main vehicle of spectrum sensing for both inference and control. By leveraging mechanisms such as data augmentation, channel attention, voting, and segmentation we are able to push beyond the capabilities of existing Deep Learning techniques and create generalizable spectrum sensing algorithms. Furthermore we deploy different spectrum sensing solutions in real testbeds for over the air evaluations and applicable proof-of-concepts.
Committee:
Prof. Tommaso Melodia (Advisor)
Prof. Francesco Restuccia
Prof. Kaushik Chowdhury