RF energy harvesting schemes for intelligent reflecting surface-aided cognitive radio sensor networks

Abstract

Energy harvesting (EH) is a potential solution to enhance the node sustainability and prolong the network lifetime of cognitive radio sensor networks (CRSNs). However, CRSNs nodes can only harvest energy from the direct link with energy sources, and severe path loss results in low energy utilization ratio. To solve the above problem, intelligent reflecting surface (IRS) is introduced, and a shared reflection coefficient matrix-based EH scheme is proposed for IRS-aided CRSNs in this paper. An optimization problem with the objective of maximizing the total amount of energy harvested by all CRSNs nodes is formulated, and by optimally adjusting the IRS reflection coefficient, CRSNs nodes can harvest energy from both the direct link and the cascaded reflection link via IRS, which increases the amount of harvested energy. In addition, a subsurface partition-based EH scheme is proposed to reduce the additional computational complexity brought by increasing IRS elements or CRSNs nodes. Simulation results show that the proposed schemes can both dramatically improve energy utilization ratio, and the subsurface partition-based EH scheme will bring in less than 1 percent performance loss when compared with the other scheme, i.e., reasonable subsurface partition can achieve a balance between harvested energy and computational complexity.