Optimal satisfaction degree in energy harvesting cognitive radio networks

A cognitive radio（CR） network with energy harvesting（EH） is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model（HMM） is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree（WSD） of the cognitive radio...

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Bibliographic Details
Published in:Chinese physics B 2015-12, Vol.24 (12), p.592-599
Main Author: 李赞 刘伯阳 司江勃 周福辉
Format: Article
Language:English
Subjects:
Cognitive radio
Energy harvesting
Mathematical models
Mixed integer
Networks
Nonlinear programming
Optimization
Quality of service architectures
优化问题
收获
无线电网络
混合整数非线性规划
满意度
能量收集
隐马尔可夫模型
高频谱效率
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Description
Summary:A cognitive radio（CR） network with energy harvesting（EH） is considered to improve both spectrum efficiency and energy efficiency. A hidden Markov model（HMM） is used to characterize the imperfect spectrum sensing process. In order to maximize the whole satisfaction degree（WSD） of the cognitive radio network, a tradeoff between the average throughput of the secondary user（SU） and the interference to the primary user（PU） is analyzed. We formulate the satisfaction degree optimization problem as a mixed integer nonlinear programming（MINLP） problem. The satisfaction degree optimization problem is solved by using differential evolution（DE） algorithm. The proposed optimization problem allows the network to adaptively achieve the optimal solution based on its required quality of service（Qos）. Numerical results are given to verify our analysis.
ISSN:1674-1056
2058-3834
1741-4199
DOI:10.1088/1674-1056/24/12/128401