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Open AccessArticle

Machine Learning for LTE Energy Detection Performance Improvement

Department of Wireless Communications, Poznan University of Technology, 61-131 Poznan, Poland
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4348;
Received: 31 August 2019 / Revised: 4 October 2019 / Accepted: 6 October 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
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. View Full-Text
Keywords: spectrum sensing; cognitive radio; machine learning; energy detection; k-nearest neighbors; random forest spectrum sensing; cognitive radio; machine learning; energy detection; k-nearest neighbors; random forest
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Wasilewska, M.; Bogucka, H. Machine Learning for LTE Energy Detection Performance Improvement. Sensors 2019, 19, 4348.

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