Comparative Performance of Algorithmic Techniques for Optimizing Dual-Band Rectifier

Abstract

Radio Frequency (RF) energy harvesting (EH) is a technique to replenish the source of wireless sensor networks (WSNs). Also, many interdisciplinary fields in the Internet-of-Things (IoT) era use RF-EH, like precision agriculture, biomedical, and robotics. Over the years, various designs have been presented in the literature operating in multi- or wide-band frequencies. Usually, a designed system is optimized using specific goals and optimization parameters to obtain maximization in power conversion efficiency (PCE). In this work, a dual-band RF rectifier system that resonates in the Wi-Fi frequency bands of 2.45 GHz and 5.8 GHz is presented. The proposed system is optimized using four optimization techniques, namely the Gradient algorithm, the Minimax algorithm, the Simulated Annealing, and the Genetic algorithm. A set of comparative results is presented to assess the performance of each technique and to obtain the feasible solution of the proposed design. Numerical results demonstrate that a 42.8% efficiency is achieved, having a 16 dBm input power and a 1.7 kΩ output resistance load.