Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/331461
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dc.coverage.spatialAdaptive learning decision making in cognitive radio networks
dc.date.accessioned2021-07-12T10:04:44Z-
dc.date.available2021-07-12T10:04:44Z-
dc.identifier.urihttp://hdl.handle.net/10603/331461-
dc.description.abstractBeing a type of intelligent communication technology, the features of dynamic spectrum allocation concept of cognitive radio renders a practical approach for spectrum resources sharing among the primary user and secondary users, which helps in resolving the present problem of spectrum resource deficit. In Cognitive Radio Networks, eventhough energy detection approach is uncomplicated with lesser sensing time, the same cannot be said for the performance, which is not reasonable under low Signal to Noise Ratio (SNR) scenarios. With the aim of rendering practical and effective spectrum allocation in wireless networks, this technical work also studies the common framework model that depends on game theory for cognitive radio spectrum allocation. The first research work, a Distribution Factor (DF) based fuzzy logic approach with spectrum sensing game theory model was proposed to minimize the bandwidth restrictions in CRN employing energy detection, which is considered to be the most simple spectrum sensing approach. The D-F based fuzzy logic mechanism works based on Signal to Noise Ratio (SNR), local sensing difference and threshold of energy detector of every sensor node. Each of these aspects are integrated applying Fuzzy Logic to improve the detection performance of the sensor nodes. As per the game theory framework, the secondary users (SU) can determine the sensing approach that has to be used depending on the utility function. The SU computes an estimate on the SNR of every channel in prior and generates the utility function in terms of the energy and throughput. After this, the sensing approach is decided in accordance depending on the SNR value, implying the Distribution Factor (DF) based fuzzy logic approach to minimize the bandwidth restrictions. But, there was no energy saving in this work. Therefore, a joint channel allocation and spectrum sensing approach has to be developed which should minimize the interference considerably and also the energy usage. newline
dc.format.extentxii,152p.
dc.languageEnglish
dc.relationp.140-151
dc.rightsuniversity
dc.titleAdaptive learning decision making in cognitive radio networks
dc.title.alternative
dc.creator.researcherArivazhagi P
dc.subject.keywordCognitive radio networks
dc.subject.keywordSignal to Noise Ratio
dc.subject.keywordFuzzy logic
dc.description.note
dc.contributor.guideKarthigai Kumar P
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Information and Communication Engineering

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02_certificates.pdf376.93 kBAdobe PDFView/Open
03_vivaproceedings.pdf547.59 kBAdobe PDFView/Open
04_bonafidecertificate.pdf601.79 kBAdobe PDFView/Open
05_abstracts.pdf119.74 kBAdobe PDFView/Open
06_acknowledgements.pdf664.07 kBAdobe PDFView/Open
07_contents.pdf231.97 kBAdobe PDFView/Open
08_listoftables.pdf129.81 kBAdobe PDFView/Open
09_listoffigures.pdf191.04 kBAdobe PDFView/Open
10_listofabbreviations.pdf226.22 kBAdobe PDFView/Open
11_chapter1.pdf1.75 MBAdobe PDFView/Open
12_chapter2.pdf1.42 MBAdobe PDFView/Open
13_chapter3.pdf1.33 MBAdobe PDFView/Open
14_chapter4.pdf1.46 MBAdobe PDFView/Open
15_chapter5.pdf1.94 MBAdobe PDFView/Open
16_conclusion.pdf261.17 kBAdobe PDFView/Open
17_references.pdf1.16 MBAdobe PDFView/Open
18_listofpublications.pdf222.92 kBAdobe PDFView/Open
80_recommendation.pdf191.42 kBAdobe PDFView/Open


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