期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2014
卷号：7
期号：3
页码：443-452
DOI：10.14257/ijsip.2014.7.3.36
出版社：SERSC
摘要：In this paper, we study spectrum sensing based on dimensionality reduction and random forest (RF) in low signal-to-noise ratio environments. Classifications of three digital modulation types, including BPSK, OFDM and 2FSK, are investigated. From the received radio signal, a set of cyclic spectrum features are first calculated, and the principal component analysis (PCA) is applied to extract the most discriminant feature vector for classification. Furthermore, the detecting signal is classified by the trained random forest, which uses the Gini index as the classification criteria, to test whether the primary user exists. The performance of our proposed PCA combining with RF algorithm is evaluated through simulations and compared with MME, SVM, RF. Experimental results show that with dimensionality reduction, the performance of classification is much better with fewer features than that of without dimensionality reduction.
关键词：Cognitive networks; spectrum sensing; principal component analysis; random ; forest
