Detection of Primary User assisted by Machine Learning over Multipath Channels

In this paper, we provide new blind spectrum sensing (SS) methods based on a machine learning (ML) model to overcome the effects of multipath channels. We introduce three ML methods in order to improve the detection of the primary user (PU) in a cognitive radio network in severe multipath environment. The ML approaches proposed here are the Naive Bayes Classifier (NBC), the Support Vector Machine (SVM) and the Artificial neural network (ANN). The non-linear separation of the training samples provided by ML features is a good alternative to avoid miss-detection or false alarm obtained with classical fixed threshold detection. Simulations shows that the proposed algorithms outperform classical non-cooperative SS algorithms based on the eigenvalues of the covariance matrix of the received signal. The proposed SS detectors based on ML algorithms are concluded to be good candidates for PU detection over multipath channels as in indoor scenarios, especially in low signal to noise ratios (SNR).