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Adaptive Multiband Spectrum Sensing for Cognitive Radio

Graduate #102
Discipline: Technology and Engineering
Subcategory: Electrical Engineering

Nancy Ronquillo - University of California, San Diego
Co-Author(s): Tara Javidi, University of California San Diego



The innovative use of spectrum sensing for cognitive radio is a methodology that aims to meet the demand for higher data rates in wireless communications by opportunistically exploiting traditionally under-utilized spectral resources. Under the assumption that the primary user occupied spectral frequency bands are significantly more popular than secondary user, spectral holes, then spectrum sensing becomes a single target detection problem. Multiband spectrum sensing has traditionally offered a solution that efficiently detects these spectral opportunities by simultaneously searching multiple spectral frequency bands in subchannel groups. This research project explores a further optimization of Multiband spectrum sensing for cognitive radio by using an iterative adaptive posterior matching target searching algorithm that expectedly outperforms a non-adaptive algorithm under the addition of a noise parameter. Experimental results are obtained by conducting computer simulations that are modeled using a Bayesian posterior probability approach. These results will quantitatively compare the posterior matching algorithm to the non-adaptive algorithm as well as to alternative adaptive algorithms in Cognitive radio literature. Preliminary results obtained using this adaptive algorithm, have already shown that it can outperform its non-adaptive counterpart. More simulations are currently being developed to further strengthen these conclusions. Future work includes the exploration of this problem without the assumption that a single target exists, a problem that can be compared to a multi-target search optimization.

Not Submitted

Funder Acknowledgement(s): Alfred P. Sloan Foundation, San Diego Fellowship Foundation

Faculty Advisor: Tara Javidi, tjavidi@ucsd.edu

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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