Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules
The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy c-means clus...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-02-01.
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| Subjects: | |
| Online Access: | Get fulltext |
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| Summary: | The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy c-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique. |
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| Item Description: | https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23085 |