Least-squares support vector machine-based learning and decision making in cognitive radios

Wu, Chun; Qi, Yu; Yi, Kechu

doi:10.1049/iet-com.2012.0205

Cited by 12 publications

(2 citation statements)

References 12 publications

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“…SVM adopts structural risk minimization principle which has been shown superior to empirical risk minimization principle used by traditional neural networks [ 24 ]. Moreover, the generalization ability of SVM is strong [ 33 ]. SVM is initially used to solve the classification problem.…”

Section: System Modelmentioning

confidence: 99%

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Wang

Zhang

et al. 2014

The Scientific World Journal

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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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Section: System Modelmentioning

confidence: 99%

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Wang

Zhang

et al. 2014

The Scientific World Journal

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“…Currently, although some literatures about how to properly determine the link parameters of the secondary user (SU) have been reported, for example, [6][7][8][9][10][11][12], there are still some problems. First, in some literatures, the SU makes link decision solely according to its quality of service (QoS) requirements and the channel characteristics without considering the influence of its behaviours on the primary users (PUs) [6][7][8]. Secondly, some of the existing work is based on the assumption that the activities of the PU have no influence on the SU's transmission [9][10][11][12][13], for example, they assume that the PU's transmission causes no interference to the SU.…”

Section: Introductionmentioning

confidence: 99%

Link decision algorithm in underlay cognitive radio networks over a Rayleigh fading channel

Tan

Zhang

2014

IET Communications

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Detection of the Primary User’s Behavior for the Intervention of the Secondary User Using Machine Learning

Soto

Parra

Sarmiento

2018

Future Data and Security Engineering

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Least-squares support vector machine-based learning and decision making in cognitive radios

Cited by 12 publications

References 12 publications

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Link decision algorithm in underlay cognitive radio networks over a Rayleigh fading channel

Detection of the Primary User’s Behavior for the Intervention of the Secondary User Using Machine Learning

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