An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio

Simple item page
dc.check.embargoformatBoth hard copy thesis and e-thesisen
dc.check.entireThesisEntire Thesis Restricted
dc.check.opt-outNot applicableen
dc.check.reasonThis thesis is due for publication or the author is actively seeking to publish this materialen
dc.contributor.advisorMurphy, Colin C.en
dc.contributor.authorLawton, Brendan
dc.contributor.funderIrish Research Council for Science Engineering and Technologyen
dc.date.accessioned2014-03-24T14:40:40Z
dc.date.issued2013
dc.date.submitted2013
dc.description.abstractThis thesis investigates the optimisation of Coarse-Fine (CF) spectrum sensing architectures under a distribution of SNRs for Dynamic Spectrum Access (DSA). Three different detector architectures are investigated: the Coarse-Sorting Fine Detector (CSFD), the Coarse-Deciding Fine Detector (CDFD) and the Hybrid Coarse-Fine Detector (HCFD). To date, the majority of the work on coarse-fine spectrum sensing for cognitive radio has focused on a single value for the SNR. This approach overlooks the key advantage that CF sensing has to offer, namely that high powered signals can be easily detected without extra signal processing. By considering a range of SNR values, the detector can be optimised more effectively and greater performance gains realised. This work considers the optimisation of CF spectrum sensing schemes where the security and performance are treated separately. Instead of optimising system performance at a single, constant, low SNR value, the system instead is optimised for the average operating conditions. The security is still provided such that at the low SNR values the safety specifications are met. By decoupling the security and performance, the system’s average performance increases whilst maintaining the protection of licensed users from harmful interference. The different architectures considered in this thesis are investigated in theory, simulation and physical implementation to provide a complete overview of the performance of each system. This thesis provides a method for estimating SNR distributions which is quick, accurate and relatively low cost. The CSFD is modelled and the characteristic equations are found for the CDFD scheme. The HCFD is introduced and optimisation schemes for all three architectures are proposed. Finally, using the Implementing Radio In Software (IRIS) test-bed to confirm simulation results, CF spectrum sensing is shown to be significantly quicker than naive methods, whilst still meeting the required interference probability rates and not requiring substantial receiver complexity increases.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLawton, B. 2013. An investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radio. PhD Thesis, University College Cork.en
dc.identifier.endpage219
dc.identifier.urihttps://hdl.handle.net/10468/1486
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2013, Brendan Lawtonen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectCognitive radioen
dc.subject.lcshSoftware radioen
dc.subject.lcshArtificial intelligenceen
dc.subject.lcshWireless communication systemsen
dc.thesis.opt-outfalse
dc.titleAn investigation on the use of SNR distributions for the optimisation of coarse-fine spectrum sensing for cognitive radioen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisorcmurphy@rennes.ucc.ie
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
thesis(1).pdf
Size:
3.72 MB
Format:
Adobe Portable Document Format
Description:
Full Text E-Thesis
Download
Thumbnail Image
Name:
Abstract.pdf
Size:
16.77 KB
Format:
Adobe Portable Document Format
Description:
Abstract
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
5.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Doctoral Theses
College of Science, Engineering and Food Science - Doctoral Theses
Electrical and Electronic Engineering - Journal Articles