Spectrum Sensing for Cognitive Network Based on Principal Component Analysis and Random Forest

摘要：

　　Aiming to the problem of weak primary user signal detection rate in low signal-to-noise ratio environments,we propose a novel spectrum sensing method based on the principal component analysis(PCA)and random forest(RF).From the received radio signal,a set of cyclic spectrum features are first calculated,and the PCA is applied to extract the most discriminate feature vector for classification.Furthermore,the detecting signal is classified by the trained random forest to test whether the primary user exists.Compares with MME,SVM,RF,our proposed algorithm is evaluated through simulations.Experimental results show that the performance of our proposed algorithm is much better than compared algorithms in low signal-to-noise ratio environments.

关键词： Cognitive network Spectrum sensing Principal component analysis Random forest

作者: Xin WANG Zhi-gang LIU Jin-kuan WANG Bin WANG Xi HU

作者单位: College of Information Science and Engineering,Northeastern University,Shenyang 110004,China;School College of Information Science and Engineering,Northeastern University,Shenyang 110004,China

会议类型: 国际会议

会议名称: 第26届中国控制与决策会议(2014 CCDC)

会议地点: 长沙

会议语种:英文

页码: 3029-3032

在线出版日期: 2014-05-31（万方平台首次上网日期，不代表论文的发表时间）