Detection of spectrum hole from n-number of primary users using machine learning algorithms

Detection of spectrum hole from n-number of primary users using machine learning algorithms

A method for detecting spectrum holes based on the n-number of primary users (PU's) in a cognitive radio environment, using a cooperative spectrum sensing model is proposed in this study. The fusion centre, senses the n-number of PUs. When the number of PUs is >200, the probability of detect...

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Bibliographic Details
Main Authors: Udayamoorthy Venkateshkumar, Srinivansan Ramakrishnan
Format: Article
Language:English
Published: Wiley 2019-11-01
Series:The Journal of Engineering
Subjects:
cognitive radio
signal detection
radio spectrum management
probability
learning (artificial intelligence)
spectrum hole
primary users
machine learning algorithms
pu
cognitive radio environment
spectrum sensing model
detection decreases
false alarm increases
random forest algorithm
rf algorithm
clustering algorithm
500 pus
maximum probability
200 pus
false alarm probability
noise figure -12.0 db to 10.0 db
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0024

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0024

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