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Permanent link (DOI): https://doi.org/10.7939/R33X37

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Analysis of Energy Detection in Cognitive Radio Networks Open Access

Descriptions

Other title
Subject/Keyword
Energy detection
Wireless communications
Spectrum sensing
Cognitive radio networks
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Atapattu, Saman U. B.
Supervisor and department
Jiang, Hai (Electrical and Computer Engineering)
Tellambura, Chintha (Electrical and Computer Engineering)
Examining committee member and department
Krzymie, Witold (Electrical and Computer Engineering)
Tellambura, Chintha (Electrical and Computer Engineering)
Khabbazian, Majid (Electrical and Computer Engineering)
Jiang, Hai (Electrical and Computer Engineering)
Dong, Xiaodai (Electrical and Computer Engineering, University of Victoria)
Department
Department of Electrical and Computer Engineering
Specialization
Communications
Date accepted
2013-08-26T07:05:44Z
Graduation date
2013-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Cognitive radio is one of the most promising technologies to address the spectrum scarcity problem. Cognitive radio requires spectrum sensing, which is used by unlicensed users to opportunistically access the licensed spectrum. Spectrum sensing using energy detection offers low-cost and low-complexity. In this thesis, a comprehensive performance analysis of energy detection based spectrum sensing is developed. Detection performance over composite (fading and shadowing) channels is first investigated using the K and K_G channel models. To further facilitate analysis of energy detection over different wireless channels, a unified channel model based on a mixture gamma distribution is developed. The unified model can accurately represent most existing channel models. A single-value performance metric, the area under the receiver operating characteristic curve, is proposed to measure the overall detection capability, and is investigated over various wireless fading channels. The energy detection based cooperative spectrum sensing is also studied, which can largely improve the detection performance. Since spectrum sensing is required to identify activities of licensed users at a very low signal-to-noise ratio (SNR), performance of energy detection with low SNR is also analyzed in this thesis.
Language
English
DOI
doi:10.7939/R33X37
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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