End-to-End Waveform and Beamforming Optimization for RF Wireless Power Transfer

TL;DR

This paper develops an optimization framework for RF wireless power transfer that jointly designs waveforms and beamforming to improve efficiency, using swarm intelligence to reduce power consumption in multi-antenna systems.

Contribution

It introduces a mathematical model for harvested power and proposes a swarm intelligence-based optimization method for joint waveform and beamforming design.

Findings

Power consumption decreases with higher DAC resolution, phase shifter resolution, and antenna length.

Increasing system frequency leads to higher power consumption.

The proposed approach effectively reduces power use while meeting charging requirements.

Abstract

Radio frequency (RF) wireless power transfer (WPT) is a key technology for future low-power wireless systems. However, the inherently low end-to-end power transfer efficiency (PTE) is challenging for practical applications. The main factors contributing to it are the channel losses, transceivers' power consumption, and losses related, e.g., to the digital-to-analog converter (DAC), high-power amplifier, and rectenna. Optimizing PTE requires careful consideration of these factors, motivating the current work. Herein, we consider an analog multi-antenna power transmitter that aims to charge a single energy harvester. We first provide a mathematical framework to calculate the harvested power from multi-tone signal transmissions and the system power consumption. Then, we formulate the joint waveform and analog beamforming design problem to minimize power consumption and meet the charging requirements. Finally, we propose an optimization approach relying on swarm intelligence to solve the specified problem. Simulation results quantify the power consumption reduction as the DAC, phase shifters resolution, and antenna length are increased, while it is seen that increasing system frequency results in higher power consumption.