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• W3048458469 abstract "To address spectrum underutilization and energy constraint in wireless communication technologies, cognitive radio networks (CRN) incorporated with energy-harvesting (EH) ability is an evergreen solution. The dynamic behavior of the primary user (PU) activity is a primary component that affects the performance of a CRN. In this chapter, we investigate the impact of distribution functions on the performance of an energy harvesting enabled cooperative CRN. We consider the two most relevant distribution functions, namely Weibull and Erlang distributions, to characterize the PU behavior in a prediction-based sensing under a cooperative EH-CRN. Cooperative CRN offers better reliability of event detection, which results in efficient spectrum utilization. In this chapter, we consider a centralized cooperative EH-CRN whereby each cognitive radio (CR) node has the capability of scavenging energy from radio frequency (RF) or non-RF sources depending on a combined decision taken by the fusion center (FC). We use conventional and estimation-based energy detection schemes in our analysis. Analytical formulae for the detection probability, harvested energy, normalized throughput, and energy penalty are established, employing OR fusion rule. The impact of prediction error, number of cooperative CR nodes, number of frames, and collision constraint on energy harvesting and normalized throughput is also studied. Simulations are performed, and a thorough, comprehensive comparison of the results is presented. A detailed comparative analysis for both Weibull and Erlang distributions is also presented. The results show that both distributions perform better than the conventional exponential distribution in a centralized cooperative EH-CRN and signify the usability of the model in designing practical systems." @default.
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• W3048458469 date "2020-08-12" @default.
• W3048458469 modified "2023-10-16" @default.
• W3048458469 title "Cooperative Spectrum Sensing in Energy Harvesting Cognitive Radio Networks Under Diverse Distribution Models" @default.
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• W3048458469 doi "https://doi.org/10.1007/978-981-15-6390-4_12" @default.
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