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Permanent link (DOI): https://doi.org/10.7939/R3ZS2KK8R
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Spectrum Sensing, Access, and Leasing in Cognitive Radio Networks Open Access
- Other title
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Jiang, Hai (Electrical and Computer Engineering)
- Examining committee member and department
Zhao, Hong Vicky (Electrical and Computer Engineering)
Qiu, Zhi-Jun (Civil and Environmental Engineering)
Department of Electrical and Computer Engineering
- Date accepted
- Graduation date
Master of Science
- Degree level
Cognitive radio has been considered as a promising way to deal with the overcrowded wireless spectrum. In cognitive radio, when licensed users (primary users) do not use their licensed spectrum, they may lease the spectrum to unlicensed users (secondary user). In a cognitive radio network, a secondary user may target at maximizing its utility, while a primary user may target at maximizing its revenue. In this thesis, the utility maximization of a secondary user and the revenue maximization of a primary user are both investigated.
For a secondary user’s utility maximization, we investigate the spectrum sensing and access strategy of the secondary user. The secondary user pays rental fee to the primary user when accessing the licensed channel. In addition, a penalty fee is charge if the secondary user fails to detect primary activities and interferes with primary reception. The setting of the penalty price is discussed. The secondary utility maximization problem is formulated, which selects the optimal spectrum sensing duration and secondary transmission power. The problem is shown to be nonconvex. Some properties of the problem are derived, and accordingly, an iterative algorithm is provided to solve the problem.
For primary user’s revenue maximization, long-term spectrum leasing with multiple rounds is considered, and the target is to find optimal price values over the rounds. Cases with discrete and continuous spectrum demand are investigated. For each case, revenue optimization problems are formulated, and solving methods are also provided. Some interesting properties of the optimal solutions are also presented as well.
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