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Spectrum Prediction in Cognitive Radio Network Using Machine Learning Techniques

D. Arivudainambi1, S. Mangairkarasi1,*, K. A. Varun Kumar2
1 Department of Mathematics, Anna University, Chennai, 600025, India
2 Department of Information Technology, B.S.Abdur Rahman Crescent Institute of Science and Technology, Vandalur–Chennai, 600048, India
* Corresponding Author: S. Mangairkarasi. Email:

Intelligent Automation & Soft Computing 2022, 32(3), 1525-1540.

Received 25 May 2021; Accepted 08 July 2021; Issue published 09 December 2021


Cognitive Radio (CR) aims to achieve efficient utilization of scarcely available radio spectrum. Spectrum sensing in CR is a basic process for identifying the existence or absence of primary users. In spectrum sensing, CR users suffer from deep fading effects and it requires additional sensing time to identify the primary user. To overcome these challenges, we frame Spectrum Prediction-Channel Allocation (SP-CA) algorithm which consists of three phases. First, clustering mechanisms to select the spectrum coordinator. Second, Eigenvalue based detection method to expand the sensing accuracy of the secondary user. Third, Bayesian inference approach to reduce the performance degradation of the secondary user. The Eigenvalue based detection method is compared with Energy detection method in terms of varying false alarm rates and samples. The Eigenvalue detection method achieves better performance than Energy detection method. The Simulation results show that our approach gives better performance in terms of reducing sensing time and increasing sensing accuracy.


Cognitive radio; spectrum sensing; spectrum prediction; Eigenvalue based detection; clustering algorithms

Cite This Article

D. Arivudainambi, S. Mangairkarasi and K. A. Varun Kumar, "Spectrum prediction in cognitive radio network using machine learning techniques," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1525–1540, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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