Space-Time-Frequency Machine Learning for Improved 4G/5G Energy Detection

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...

Full description

Saved in:
Bibliographic Details
Published in:International Journal of Electronics and Telecommunications 2020-01, Vol.66 (1), p.217-223
Main Authors: Wasilewska, Małgorzata, Bogucka, Hanna
Format: Article
Language:English
Subjects:
4G mobile communication
cognitive radio
Communications systems
energy detection
k-nearest neighbors
lte
Machine learning
Radio communications
random forest
spectrum sensing
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2081-8491
2300-1933
DOI:10.24425/ijet.2020.131866