RF Energy Transfer Channel Models for Sustainable IoT

Abstract

Self-sustainability of wireless nodes in Internet-of-Things applications can be realized with the help of controlled radio frequency energy transfer (RF-ET). However, due to significant energy loss in wireless dissipation, there is a need for novel schemes to improve the end-to-end RF-ET efficiency. In this paper, first we propose a new channel model for accurately characterizing the harvested dc power at the receiver. This model incorporates the effects of nonline of sight (NLOS) component along with the other factors, such as radiation pattern of transmit and receive antennas, losses associated with different polarization of transmitting field, and efficiency of power harvester circuit. Accuracy of the model is verified via experimental studies in an anechoic chamber (a controlled environment). Supported by experiments in controlled environment, we also formulate an optimization problem by accounting for the effect of NLOS component to maximize the RF-ET efficiency, which cannot be captured by the Friis formula. To solve this nonconvex problem, we present a computationally efficient golden section-based iterative algorithm. Finally, through extensive RF-ET measurements in different practical field environments we obtain the statistical parameters for Rician fading as well as path loss factor associated with shadow fading model, which also asserts the fact that Rayleigh fading is not well suited for RF-ET due to presence of a strong line of sight component.