An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks

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dc.contributor.advisorDlodlo, Mqhele Een_ZA
dc.contributor.authorNgethe, Nixon Thuoen_ZA
dc.date.accessioned2016-06-23T14:50:14Z
dc.date.available2016-06-23T14:50:14Z
dc.date.issued2015en_ZA
dc.description.abstractThe paradigm of wireless sensor networks (WSNs) has gained a lot of popularity in the recent years due to the proliferation of wireless devices. This is evident as WSNs find numerous application areas in fields such as agriculture, infrastructure monitoring, transport, and security surveillance. Traditionally, most deployments of WSNs operate in the unlicensed industrial scientific and medical (ISM) band and more specifically, the globally available 2.4 GHz frequency band. This band is shared with several other wireless technologies such as Bluetooth, Wi-Fi, near field communication and other proprietary technologies thus leading to overcrowding and interference problems. The concept of dynamic spectrum access alongside cognitive radio technology can mitigate the coexistence issues by allowing WSNs to dynamically access new spectrum opportunities. Furthermore, cognitive radio technology addresses some of the inherent constraints of WSNs thus introducing a myriad of benefits. This justifies the emergence of cognitive radio sensor networks (CRSNs). The selection of a spectrum sensing technique plays a vital role in the design and implementation of a CRSN. This dissertation sifts through the spectrum sensing techniques proposed in literature investigating their suitability for CRSN applications. We make amendments to the conventional energy detector particularly on the threshold selection technique. We propose an adaptive threshold energy detection model with noise variance estimation for implementation in CRSN systems. Experimental work on our adaptive threshold technique based on the recursive one-sided hypothesis test (ROHT) technique was carried out using MatLab. The results obtained indicate that our proposed technique is able to achieve adaptability of the threshold value based on the noise variance. We also employ the constant false alarm rate (CFAR) threshold to act as a defence mechanism against interference of the primary user at low signal to noise ratio (SNR). Our evaluations indicate that our adaptive threshold technique achieves double dynamicity of the threshold value based on the noise variance and the perceived SNR.en_ZA
dc.identifier.apacitationNgethe, N. T. (2015). <i>An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/20103en_ZA
dc.identifier.chicagocitationNgethe, Nixon Thuo. <i>"An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015. http://hdl.handle.net/11427/20103en_ZA
dc.identifier.citationNgethe, N. 2015. An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Ngethe, Nixon Thuo AB - The paradigm of wireless sensor networks (WSNs) has gained a lot of popularity in the recent years due to the proliferation of wireless devices. This is evident as WSNs find numerous application areas in fields such as agriculture, infrastructure monitoring, transport, and security surveillance. Traditionally, most deployments of WSNs operate in the unlicensed industrial scientific and medical (ISM) band and more specifically, the globally available 2.4 GHz frequency band. This band is shared with several other wireless technologies such as Bluetooth, Wi-Fi, near field communication and other proprietary technologies thus leading to overcrowding and interference problems. The concept of dynamic spectrum access alongside cognitive radio technology can mitigate the coexistence issues by allowing WSNs to dynamically access new spectrum opportunities. Furthermore, cognitive radio technology addresses some of the inherent constraints of WSNs thus introducing a myriad of benefits. This justifies the emergence of cognitive radio sensor networks (CRSNs). The selection of a spectrum sensing technique plays a vital role in the design and implementation of a CRSN. This dissertation sifts through the spectrum sensing techniques proposed in literature investigating their suitability for CRSN applications. We make amendments to the conventional energy detector particularly on the threshold selection technique. We propose an adaptive threshold energy detection model with noise variance estimation for implementation in CRSN systems. Experimental work on our adaptive threshold technique based on the recursive one-sided hypothesis test (ROHT) technique was carried out using MatLab. The results obtained indicate that our proposed technique is able to achieve adaptability of the threshold value based on the noise variance. We also employ the constant false alarm rate (CFAR) threshold to act as a defence mechanism against interference of the primary user at low signal to noise ratio (SNR). Our evaluations indicate that our adaptive threshold technique achieves double dynamicity of the threshold value based on the noise variance and the perceived SNR. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks TI - An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks UR - http://hdl.handle.net/11427/20103 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/20103
dc.identifier.vancouvercitationNgethe NT. An adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networks. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/20103en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleAn adaptive threshold energy detection technique with noise variance estimation for cognitive radio sensor networksen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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