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Co-variance Based Adaptive Threshold Spectrum Detection Optimized with Chameleon Swarm Optimization for Optimum Threshold Selection in Cognitive Radio Networks

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Abstract

In cognitive radio networks, Spectrum sensing is most important task for avoiding the unacceptable interference to primary users. The performance of spectrum sensing is based on the threshold value used in the sensing scheme. The existing co-variance based spectrum sensing technique use a fixed threshold which does not ensure sufficient protection to Primary users. Hence, Co-variance based adaptive threshold spectrum detection optimized with Chameleon Swarm optimization for Optimum threshold selection in Cognitive Radio Networks is proposed for appropriate security to the primary user and also reduces the total error probability. In this, the threshold selection is based on constant false alarm rate (CFAR) principles and constant detection rate (CDR) principles. Besides these principles the threshold can be derived by reducing the total probability of decision error. Hence co-variance based adaptive threshold detection (CATD) process is proposed to obtain the adaptive threshold for minimizing the total error probability. But the performance of this process weakens at low Signal to Noise Ratio. To overcome these issues, the optimized selection of threshold is needed. Hence Chameleon swarm optimization algorithm (CSOA) is proposed to enhance the performance of CATD process by selecting an optimal threshold value. The simulation process is executed in the MATLAB platform. The proposed Co-variance based adaptive threshold spectrum detection optimized with Chameleon Swarm optimization (CATD-CSOA) attains low error probability 38.5%, high detection probability 98.6, and high throughput 94.35% when comparing with the existing method such as Optimum threshold selection in Spectrum sensing based Adaptive Covariance-based Detection Algorithm (ACDA) and Optimum threshold selection in Spectrum sensing based CUSUM (Cumulate Sum) Algorithm. Finally, the proposed CATD-CSOA produces high detection probability with limited number of samples as well as threshold is selected in an optimized way.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to these data are depended to scheme and value of chosen threshold but are available from the corresponding author on reasonable request.

Code Availability

The programming code is available with the authors.

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“The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.”

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“All authors contributed to the study conception and problem formulation and solution. The problem formulation and the proposal of a new scheme are performed by Prof RP and Dr CC, respectively. The first draft of the manuscript was written by Dr Chhagan Charan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”

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Correspondence to Chhagan Charan.

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Charan, C., Pandey, R. Co-variance Based Adaptive Threshold Spectrum Detection Optimized with Chameleon Swarm Optimization for Optimum Threshold Selection in Cognitive Radio Networks. Wireless Pers Commun 132, 1025–1047 (2023). https://doi.org/10.1007/s11277-023-10647-2

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