Spectrum sensing in cognitive radio networks: threshold optimization and analysis

Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the...

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Vydáno v:EURASIP journal on wireless communications and networking Ročník 2020; číslo 1; s. 1 - 19
Hlavní autoři: kockaya, Kenan, Develi, Ibrahim
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 12.12.2020
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Cognitive radio
Communications Engineering
Distance learning
Energy detection
Engineering
Fading
Information Systems Applications (incl.Internet)
Machine learning
Machine learning algorithm
Matched filters
Networks
Online learning algorithm
Optimization
Radio networks
Radio spectra
Signal to noise ratio
Signal,Image and Speech Processing
Spectrum allocation
Spectrum sensing
Threshold
Spectrum sensing
Energy detection
Machine learning algorithm
Threshold
Online learning algorithm
ISSN:1687-1499, 1687-1472, 1687-1499
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Shrnutí:Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.
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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-020-01870-7