@ARTICLE{Wasilewska_Małgorzata_Space-Time-Frequency_2020, author={Wasilewska, Małgorzata and Bogucka, Hanna}, volume={vol. 66}, number={No 1}, journal={International Journal of Electronics and Telecommunications}, pages={217-223}, howpublished={online}, year={2020}, publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications}, abstract={In this paper, the future Fifth Generation (5G New Radio) radio communication system has been considered, coexisting and sharing the spectrum with the incumbent Fourth Generation (4G) Long-Term Evolution (LTE) system. The 4G signal presence is detected in order to allow for opportunistic and dynamic spectrum access of 5G users. This detection is based on known sensing methods, such as energy detection, however, it uses machine learning in the domains of space, time and frequency for sensing quality improvement. Simulation results for the considered methods: k-Nearest Neighbors and Random Forest show that these methods significantly improves the detection probability.}, type={Article}, title={Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection}, URL={http://journals.pan.pl/Content/115193/PDF/29_2020.pdf}, doi={10.24425/ijet.2020.131866}, keywords={spectrum sensing, cognitive radio, machine learning, energy detection, 4G, LTE, 5G, k-nearest neighbors, random forest}, }