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URBAN, R. STEINBAUER, M.
Originální název
Optimization of Machine Learning Parameters for Spectrum Survey Analysis
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper shows preliminary results of the optimization of machine learning parameters for cognitive radio application by brutal force calculations. We were analyzing frequency occupancy data of the huge measurement campaign of the spectrum background. For these date there are two possible states. Firstly, limited frequency band is occupied (detected signal level is above the threshold) by the other frequency signal | there will be an interference for our system for this frequency band. Secondly, the frequency band is free of any other wireless radiation. These true/false data are analyzed in a context of the cognitive radio by the reinforcement learning and simple learning. Each channel received a score from the learning algorithm given by weighting function. The quality of the output scores is discussed in this paper according to the learning algorithm parameters and optional learning time.
Klíčová slova
machine learning, cognitive radio, spectrum analysis
Autoři
URBAN, R.; STEINBAUER, M.
Rok RIV
2014
Vydáno
30. 9. 2014
ISBN
978-1-934142-28-8
Kniha
Proceedings of PIERS 2014 in Guangzhou
ISSN
1559-9450
Periodikum
Progress In Electromagnetics
Stát
Spojené státy americké
Strany od
612
Strany do
615
Strany počet
4
BibTex
@inproceedings{BUT109329, author="Robert {Urban} and Miloslav {Steinbauer}", title="Optimization of Machine Learning Parameters for Spectrum Survey Analysis", booktitle="Proceedings of PIERS 2014 in Guangzhou", year="2014", journal="Progress In Electromagnetics", pages="612--615", isbn="978-1-934142-28-8", issn="1559-9450" }