Leveraging Compressive Sampling and Machine Learning for Adaptive and Cooperative Wideband Spectrum Sensing Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9019s751j

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • 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, I show that the proposed technique outperforms existing approaches by making accurate spectrum occupancy decisions with lesser sensing communication and energy overheads.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Rights Statement
Peer Reviewed
Language
Replaces

Relationships

Parents:

This work has no parents.

Last modified

Downloadable Content

Download PDF

Items