Next Article in Journal
Beehive-Inspired Information Gathering with a Swarm of Autonomous Drones
Next Article in Special Issue
PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data
Previous Article in Journal
Improving the GRACE Kinematic Precise Orbit Determination Through Modified Clock Estimating
Previous Article in Special Issue
An FPGA-Based Neuro-Fuzzy Sensor for Personalized Driving Assistance
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; https://doi.org/10.3390/s19194348
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
Show Figures

Figure 1

MDPI and ACS Style

Wasilewska, M.; Bogucka, H. Machine Learning for LTE Energy Detection Performance Improvement. Sensors 2019, 19, 4348.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop