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 SubmittedFunder Acknowledgement(s): Alfred P. Sloan Foundation, San Diego Fellowship Foundation
Faculty Advisor: Tara Javidi, tjavidi@ucsd.edu