Spatio-Temporal Spectrum Sensing in Cognitive Radio Networks Using Beamformer-Aided SVM Algorithms

IEEE Access

Olusegun Peter Awe¹  Sangarapillai Lambotharan²  Anastasios Deligiannis² 

[1] Department of Electronic and Electrical Engineering, Obafemi Awolowo University, Ife, Nigeria;Wolfson School, Loughborough University, Loughborough, U.K.;

关键词: Cognitive radio;    spectrum sensing;    support vector machine;    beamforming;    multiple primary users;   

DOI  :  10.1109/ACCESS.2018.2825603

来源: DOAJ

【 摘 要 】

This paper addresses the problem of spectrum sensing in multi-antenna cognitive radio system using the support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenarios as a multiple state signal detection problem. Next, we propose a novel beamformer-aided feature realization strategy for enhancing the capability of the SVM for signal classification under both single and multiple primary users conditions. Then, we investigate the error correcting output codes-based multi-class SVM algorithms and provide a multiple independent model alternative for solving the multiple state spectrum sensing problem. The performance of the proposed detectors is quantified in terms of probability of detection, probability of false alarm, receiver operating characteristics (ROC), area under ROC curves, and overall classification accuracy. Simulation results show that the proposed detectors are robust to both temporal and joint spatio-temporal detection of spectrum holes in cognitive radio networks.

【 授权许可】

Unknown