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MS Final Examination – Adem Zaid


Wednesday, March 15, 2017 2:00 PM - 4:00 PM

When Machine Learning Meets Compressive Sampling for Efficient Cooperative Wideband Spectrum Sensing
This thesis proposes a novel technique that exploits spectrum occupancy behaviors inherent to wideband spectrum access to enable efficient cooperative spectrum sensing. The proposed technique reduces the number of required sensing measurements while accurately recovering spectrum occupancy information. It does so by leveraging compressive sampling theory to exploit the block-like occupancy structure of wideband spectrum access. The proposed technique is also adaptive in that it accounts for the variability of spectrum occupancy over time. It does so by leveraging supervised learning models to provide and use accurate, real time estimates of the spectrum occupancy. Using simulations, we show that the proposed technique outperforms existing approaches by making accurate spectrum occupancy decisions with lesser sensing communication and energy overheads.

Major Advisor: Bechir Hamdaoui
Committee: Lizhong Chen
Committee: Larry Cheng
GCR: Kyle Niemeyer


Kelley Engineering Center (campus map)
1007
Calvin Hughes
1 541 737 3617
Calvin.Hughes at oregonstate.edu
Sch Elect Engr/Comp Sci
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