Superior Selective Reporting-Based Spectrum Sensing in Energy Harvesting-Aided HCRNs

Abstract

In the present contribution we investigate the performance of superior selective reporting (SSR) for cooperative spectrum sensing in an energy harvesting-enabled multi-channel heterogeneous cognitive radio network (HCRN). To this end, we first analyze the throughput of the SSR and the optimal conventional cooperative sensing (CCS). Then, we formulate a nonlinear integer programming problem to find a throughput-optimal set of spectrum sensors scheduled to sense a particular channel, under primary user (PU) interference and energy harvesting constraints. In this context, we derive a solution based on the cross entropy (CE) method, and compare its performance with the exhaustive-search method counterpart. Furthermore, we study the tradeoff between the channel available time and detection accuracy of the SSR and CCS schemes. It is shown that this inherent tradeoff is between the channel available time and the detection accuracy. Furthermore, it is shown that as the number of spectrum sensors increases, the channel available time turns out to be the system's limiting factor in HCRNs.