Waveform Optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer

TL;DR

This paper develops scalable waveform optimization algorithms for large-scale multi-antenna multi-sine wireless power transfer, demonstrating significant performance gains over linear models especially with many tones.

Contribution

It introduces novel multiuser waveform optimization algorithms for large-scale WPT, leveraging nonlinear rectifier models and asymptotic analysis for reduced complexity.

Findings

Algorithms achieve near-optimal output voltage with many antennas.

Nonlinear model-based design outperforms linear models with many tones.

Performance gap between algorithms diminishes as the number of antennas increases.

Abstract

Wireless power transfer (WPT) is expected to be a technology reshaping the landscape of low-power applications such as the Internet of Things, machine-to-machine communications and radio frequency identification networks. Although there has been some progress towards multi-antenna multi-sine WPT design, the large-scale design of WPT, reminiscent of massive multiple-input multiple-output (MIMO) in communications, remains an open problem. Considering the nonlinear rectifier model, a multiuser waveform optimization algorithm is derived based on successive convex approximation (SCA). A lower-complexity algorithm is derived based on asymptotic analysis and sequential approximation (SA). It is shown that the difference between the average output voltage achieved by the two algorithms can be negligible provided the number of antennas is large enough. The performance gain of the nonlinear model based design over the linear model based design can be large, in the presence of a large number of tones.