The unlicensed user, or secondary user (SU), is equipped with intelligent technologies to sense the frequency spectrum. If a channel is sensed as idle (unused by the licensed user, or primary user (PU)), the SU starts communications and continues unless the PU starts to transmit over the channel. In this situation, the SU has several options: switch to a different idle channel, stop transmission and start sensing the channel, or continue the transmission over the same channel with modified parameters, including a modulation scheme and lower transmission power to minimize interference with the PU [3; 4; 5]. Therefore, spectrum sensing is paramount in cognitive radio networks. Different techniques proposed for spectrum sensing include matched filtering [6], energy detectors [7; 8; 9; 10], spectral correlation (also known as cyclostationarity-based spectrum sensing) [11; 12], radio identification [13], and waveform-based sensing [14]. Energy detector is the simplest technique measuring only the received signal power to sense the presence of PU signal. However, the classification efficiency may degrade significantly due to noise in low SNRs [7; 15].  
Machine learning (ML) techniques, on the other hand, are heuristic when learning about the nearby environment. However, an amount of historical labeled data is required to train the classifier. The test observations are classified based on parameters determined in the training phase. These techniques have found application in distinct fields, such as signal processing, economics and statistics, in classification and regression problems, and so forth [16]. The heuristic nature of ML techniques encourages employing them in cognitive radio networks as well. In addition, these techniques can achieve satisfactory performance when applied to spectrum sensing classification [17].  
The ML-based classification and regression algorithms include k-nearest neighbor, decision trees, naive Bayes, logistic regression, support vector machines (SVMs), k-means clustering, neural networks, and so on [18; 19]. Usually, the SVM-based classifier performs better, compared to other techniques, in practical problems due to the kernel function trick [20; 21; 22]. The problems that are not classifiable in the feature space are transformed to a high-dimensional space where classification is possible using a linear hyperplane. In addition, the SVM can be analyzed theoretically, which gives designers a better understanding of the classifier [23]. In this work, we adopt an SVM-based classifier to classify the results of spectrum sensing into one of a set of predefined categories.  
Furthermore, the dimensions of input observation to a classifier have a profound influence on the performance of classifier in terms of computation costs and accuracy. Instead, the features that characterize the input vectors are used as input observations [23]. In the present work, the received sensed signal with a length of 300 data elements is reduced to a three-dimensional vector by extracting three features.  
Moreover, the SU nodes and the sensors installed in cognitive sensor networks are equipped with a limited battery capacity. The increase in data traffic due to spectrum sensing and then data processing in order to predict PU activity will increase energy consumption. It is difficult to replace the batteries due to the remote location of the sensors. Energy harvesting is a promising solution to the battery limitation problems [24].