Abstract |
Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users ( CUs) and the working activities of primary users ( PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.
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Authors | Yao Wang, Zhongzhao Zhang, Lin Ma, Jiamei Chen |
Journal | TheScientificWorldJournal
(ScientificWorldJournal)
Vol. 2014
Pg. 395212
( 2014)
ISSN: 1537-744X [Electronic] United States |
PMID | 25143975
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Topics |
- Algorithms
- Animals
- Artificial Intelligence
- Humans
- Movement
- Support Vector Machine
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