# XL-MIMO Channel Modeling and Prediction for Wireless Power Transfer

## TL;DR

This paper explores XL-MIMO channel modeling and prediction techniques to enhance wireless power transfer efficiency, especially for passive nodes, using measured data and geometric predictions.

## Contribution

It compares different beamforming methods and introduces a geometry-based prediction approach for multipath components in XL-MIMO systems.

## Key findings

- A 1W transmit power transfers over 1mW at 12.3m distance.
- Geometry-based beamformer with predicted SMCs has only 2dB loss compared to perfect CSI.
- XL-MIMO can effectively transfer power over 12 meters with high efficiency.

## Abstract

Massive antenna arrays form physically large apertures with a beam-focusing capability, leading to outstanding wireless power transfer (WPT) efficiency paired with low radiation levels outside the focusing region. However, leveraging these features requires accurate knowledge of the multipath propagation channel and overcoming the (Rayleigh) fading channel present in typical application scenarios. For that, reciprocity-based beamforming is an optimal solution that estimates the actual channel gains from pilot transmissions on the uplink. But this solution is unsuitable for passive backscatter nodes that are not capable of sending any pilots in the initial access phase. Using measured channel data from an extremely large-scale MIMO (XL-MIMO) testbed, we compare geometry-based planar wavefront and spherical wavefront beamformers with a reciprocity-based beamformer, to address this initial access problem. We also show that we can predict specular multipath components (SMCs) based only on geometric environment information. We demonstrate that a transmit power of 1W is sufficient to transfer more than 1mW of power to a device located at a distance of 12.3m when using a (40x25) array at 3.8GHz. The geometry-based beamformer exploiting predicted SMCs suffers a loss of only 2dB compared with perfect channel state information.

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Source: https://tomesphere.com/paper/2302.11969