Impact of Weibull Distribution on Prediction Based Sensing in Energy Harvesting Cooperative CRN

Abstract

The spectral utilization and energy efficiency are two major factors that are predominantly driving the future of wireless communication. Cognitive Radio Network (CRN) along with energy harvesting is an encouraging solution to overcome spectrum under-utilization and energy paucity. In this paper, performance of prediction based sensing in a cooperative CRN is investigated under OR fusion rule in a binary hypothesis model. The role of distribution function on performance of such model is enormous. In this paper, we introduced Weibull distribution function in characterizing the primary user activity and analyze the performance of various parameters. Spectrum efficiency and quality of service of PUs are improved by using prediction based sensing CRN. Cognitive radio (CR) node have ability to harvest energy either from radio frequency (RF) sources or from non-RF sources. CR node starts transmission and harvests energy based on modify decision of fusion center. Impact of number of cooperative CR nodes, number of frames, prediction error, sensing time, collision constraint and splitting parameter on throughput and energy harvesting are investigated. Analytical expression for detection probability, false alarm probability, betterment in spectrum reuse, normalized throughput, energy harvesting together with energy penalty are derived, and the results are presented in the paper.