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Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

P. Gnanasivam1, G. T. Bharathy1,*, V. Rajendran2, T. Tamilselvi1

1 Department of Electronics and Communication Engineering, Jerusalem College of Engineering, Pallikaranai, Chennai, 600100, Tamil Nadu, India
2 Department of Electronics and Communication Engineering, Vels Institute of Science, Technology & Advanced Studies, Pallavaram, Chennai, 600117, Tamil Nadu, India

* Corresponding Author: G. T. Bharathy. Email:

Computer Systems Science and Engineering 2023, 44(1), 55-65.


Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximum-to-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.


Cite This Article

P. Gnanasivam, G. T. Bharathy, V. Rajendran and T. Tamilselvi, "Efficient centralized cooperative spectrum sensing techniques for cognitive networks," Computer Systems Science and Engineering, vol. 44, no.1, pp. 55–65, 2023.

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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