Charging RF-Energy Harvesting Devices in IoT Networks With Imperfect CSI

Abstract

This article considers energy delivery by a hybrid access point (HAP) to one or more radio-frequency (RF)-energy harvesting devices. Unlike prior works, it considers imperfect and causal channel state information (CSI) and probabilistic constraints that ensure devices receive their required amount of energy over a given planning horizon. To this end, it outlines two novel contributions. The first is a chance-constrained program, which is then solved using a mixed-integer linear program (MILP) coupled with a sample average approximation (SAA) method. The second is a model predictive control (MPC) solution that utilizes the Gaussian mixture model (GMM) and a so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">backoff</i> that is used to tighten probabilistic constraints. The results show that the performance of the MPC-based solution is within 8% of the optimal solution with a probability of 90.8%.