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| Content Provider | IET Digital Library |
|---|---|
| Author | Venkateshkumar, Udayamoorthy Ramakrishnan, Srinivansan |
| Abstract | 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. |
| Starting Page | 175 |
| Ending Page | 188 |
| Page Count | 14 |
| Volume Number | 2020 |
| e-ISSN | 20513305 |
| Issue Number | Issue 5, May (2020) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/5 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0024 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-11-12 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | 200 PUs Clustering Algorithm Cognitive Radio Cognitive Radio Environment Detection Decreases False Alarm Increases False Alarm Probability Knowledge Engineering Technique Learning in AI Machine Learning Algorithm Maximum Probability Noise Figure -12.0 DB to 10.0 DB Primary User Probability PU Radio Link And Equipment Radio Spectrum Management Random Forest Algorithm RF Algorithm Signal Detection Spectrum Hole Spectrum Sensing Model Statistics |
| Content Type | Text |
| Resource Type | Article |
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