Machine Learning for LTE Energy Detection Performance Improvement

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 d...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-10, Vol.19 (19), p.4348
Main Authors: Wasilewska, Małgorzata, Bogucka, Hanna
Format: Article
Language:English
Subjects:
Algorithms
Clinical decision making
Cognitive radio
Datasets
Decision making
Energy
energy detection
Energy value
False alarms
Hypotheses
k-nearest neighbors
Machine learning
Methods
Neural networks
Noise
Probability
Radio communications
random forest
spectrum sensing
Support vector machines
Wireless networks
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19194348