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SVM-based spectrum mobility prediction scheme in mobile cognitive radio networks.

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.
AuthorsYao Wang, Zhongzhao Zhang, Lin Ma, Jiamei Chen
JournalTheScientificWorldJournal (ScientificWorldJournal) Vol. 2014 Pg. 395212 ( 2014) ISSN: 1537-744X [Electronic] United States
PMID25143975 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
Topics
  • Algorithms
  • Animals
  • Artificial Intelligence
  • Humans
  • Movement
  • Support Vector Machine

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