Case study of TV spectrum sensing model based on machine learning techniques

Ain Shams Engineering Journal

Esam Abdel-Raheem¹  Abdalaziz Mohammad²  Faroq Awin³ 

[1] Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B3P4, Canada;Department of Electrical and Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B3P4, Canada;Department of Electrical and Electronic Engineering, University of Tripoli, 13555, Libya;

关键词: Spectrum sensing;    Cognitive radio;    Machine learning;    SVM;    kNN;    TD;   

DOI  :  

来源: DOAJ

【 摘 要 】

Spectrum sensing is an essential component in cognitive radios (CR). Machine learning (ML) algorithms are powerful techniques for designing a promising spectrum sensing model. In this work, the supervised ML algorithms, support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT) are applied to detect the existence of primary users (PU) over the TV band. Moreover, the Principal Component Analysis (PCA) is incorporated to speed up the learning of the classifiers. Furthermore, the ensemble classification-based approach is employed to enhance the classifier predictivity and performance. Simulation results have shown that the highest performance is achieved by the ensemble classifier. Moreover, simulation results have shown that employing PCA reduces the duration of training while maintaining the performance.

【 授权许可】

Unknown