Energy harvesting aided localization for green cognitive radio network
V. Greshma, T. Sudha · 2017
Cognitive radio networks (CRNs) has emerged as an apt solution for the spectrum scarcity problem by opportunistically accessing the underutilized licensed spectrum. Spectrum sensing in CRNs found to be the most crucial and energy demanding task. As cognitive radios are battery powered and energy is a major constraint, energy harvesting technique gained immense prominence in the design of future green CRNs. Thus, energy efficiency as well as spectrum efficiency can be guaranteed via incorporating secondary users (SU) with energy harvesting capability. Also, primary user (PU) localization improves the CRN network performance in terms of reliability, but the energy efficiency and localization accuracy trade-off have to be considered seriously. Thus, in this paper we propose an energy harvesting cognitive radio network model for the minimization of energy consumption during the spectrum sensing phase of PU localization and thereby enhancing the network lifetime as well as the transmission opportunity of the SUs without compromising localization accuracy. Furthermore, we consider a cooperative scenario employing hard decision fusion scheme concentrating on k-out-of-N rule and optimized the k value for further energy efficiency improvement. Numerical results confirm that the proposed system outperforms the existing system in terms of energy efficiency and prolonged battery life. Hence, as a whole this work contributes for the green communication network design via cognitive dimension.