NLOS Transmission Analysis for Mobile SLIPT Using Resonant Beam

TL;DR

This paper develops analytical models and simulation tools for NLOS transmission in resonant beam-based SLIPT, enabling high-power wireless charging and data transfer in mobile scenarios with arbitrary receiver positions.

Contribution

It introduces new analytical models and efficient simulation methods for NLOS RB-SLIPT, including a multi-hop sliding window approach to reduce computational load.

Findings

Achieves 4W charging power over 2m in NLOS conditions.

Demonstrates 12 bit/s/Hz data rate in NLOS scenarios.

Provides accurate beam profile and transmission loss models for mobile channels.

Abstract

Simultaneous lightwave information and power transfer (SLIPT) is a potential way to meet the demands of sustainable power supply and high-rate data transfer in next-generation networks. Although resonant beam-based SLIPT (RB-SLIPT) can realize high-power energy transfer, high-rate data transfer, human safety, and self-alignment simultaneously, mobile transmission channel (MTC) analysis under non-line-of-sight (NLOS) propagation has not been investigated. In this paper, we propose analytical models and simulation tools for reflector-assisted NLOS transmission of RB-SLIPT, where transmission loss and accurate beam field profile of NLOS MTC can be obtained with a receiver at arbitrary positions and attitude angles. We establish analytical models relying on full diffraction theory for beam propagation between tilted or off-axis planes. Then, we provide three numerical methods (i.e., NUFFT-based, cubic interpolation-based, and linear interpolation-based methods) in simulations. Moreover, to deal with the contradiction between limited computing memory and high sampling requirements for long-range transmission analysis, we propose a multi-hop sliding window approach, which can reduce the sampling number by a factor of thousands. Finally, numerical results demonstrate that RB-SLIPT can achieve $4$W charging power and $12$bit/s/Hz data rate over $2$m distance in NLOS scenarios.