Joint link learning and cognitive radio sensing.
2012-06
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Joint link learning and cognitive radio sensing.
Authors
Published Date
2012-06
Publisher
Type
Thesis or Dissertation
Abstract
In this thesis, novel cooperative spectrum sensing algorithms for cognitive radios (CRs)
are developed, which can blindly learn the channel gains between CRs and licensed primary
users (PUs), while jointly detecting active PU transmitters at each time instant.
A dictionary learning approach is taken to decompose the received signal energy samples
per CR into linear combinations of channel gains and PU transmit-powers, up to
scaling ambiguity. In addition to a iterative batch baseline algorithm, an efficient online
implementation that can track slow variation of channel gains with reduced computational
complexity is developed, as well as a distributed alternative, which requires only
local message passing among neighbors in CR networks. Two approaches for selecting
the sparsity parameter in the batch, online and distributed learning cases are also developed.
In order to remove scaling ambiguity from the columns of the channel gain
matrix, an assumption that the PU transmit-powers take values from the known set of
finite levels is made. Again, dictionary learning approach is used and batch and online
algorithms are developed. We have shown through numerical results that recovery of
channel gains and PU transmit-powers is possible.
Keywords
Description
University of Minnesota M.S. thesis. June 2012. Major: Electrical Engineering. Advisor:Prof. Georgios B. Giannakis. 1 computer file (PDF); vi, 32 pages, appendix A.
Related to
Replaces
License
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Jain, Nitin. (2012). Joint link learning and cognitive radio sensing.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/132225.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.