Detection of spectrum hole from n-number of primary users using machine learning algorithms
| Autor: | Udayamoorthy Venkateshkumar, Srinivansan Ramakrishnan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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 Engineering (General). Civil engineering (General) TA1-2040 |
| Zdroj: | The Journal of Engineering (2019) |
| Druh dokumentu: | article |
| ISSN: | 2051-3305 |
| DOI: | 10.1049/joe.2019.0024 |
| Popis: | 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 detection decreases, while the probability of a false alarm increases. The authors use the random forest (RF) algorithm to classify a customised dataset of 600 training samples. Further, they compare the RF algorithm and the k-means clustering algorithm, using test datasets with a minimum of ten PUs and a maximum of 500 PUs. Five different signal features are considered as the attributes in the proposed model. The maximum probability of detection is achieved using the k-means clustering algorithm in the case of 200 PUs and is 99.17%, while the false alarm probability is 0.8%. The receiver operating characteristic curves indicated that probability of detecting a spectrum hole in the case of the dataset with 500 PUs is 97.67% with the signal to noise ratio ranging from 10 to −12 dB. The accuracy can be increased if the number of clusters formed is increased, depending on the number of test samples. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|
Plný text