The growing number of radio communication devices and limited spectrum resources are drivers for the development of new techniques of dynamic spectrum access and spectrum sharing. In order to make use of the spectrum opportunistically, the concept of cognitive radio was proposed, where intelligent decisions on transmission opportunities are based on spectrum sensing. In this paper, two Machine Learning (ML) algorithms, namely k-Nearest Neighbours and Random Forest, have been proposed to increase spectrum sensing performance. These algorithms have been applied to Energy Detection (ED) and Energy Vector-based data (EV) to detect the presence of a Fourth Generation (4G) Long-Term Evolution (LTE) signal for the purpose of utilizing the available resource blocks by a 5G new radio system. The algorithms capitalize on time, frequency and spatial dependencies in daily communication traffic. Research results show that the ML methods used can significantly improve the spectrum sensing performance if the input training data set is carefully chosen. The input data sets with ED decisions and energy values have been examined, and advantages and disadvantages of their real-life application have been analyzed.
Keywords: cognitive radio; energy detection; k-nearest neighbors; machine learning; random forest; spectrum sensing.