Near-Field Energy Harvesting Using XL-MIMO Over Non-Stationary Channels

TL;DR

This paper presents a joint optimization framework for near-field energy harvesting in XL-MIMO systems, improving power efficiency by selectively activating sub-arrays and optimizing power control, with near-optimal performance and reduced complexity.

Contribution

It introduces a multi-tier fractional programming approach for sub-array selection and power control in non-stationary XL-MIMO channels, enhancing energy harvesting efficiency.

Findings

Achieves up to 120% gain over benchmark schemes.

Near-optimal performance comparable to exhaustive search.

Reduces computational complexity significantly.

Abstract

This paper explores the maximization of the harvested power efficiency (HPE) in a modular extremely large multiple-input multiple-output (XL-MIMO) system, which supports energy harvesting (EH) for near-field users. These users are located in spatially distinct visibility regions (VRs) with non-stationary channel characteristics. We propose to determine which sub-arrays are switched on or off as well the power control coefficients at the sub-arrays to maximize the HPE. The design can be processed via a multi-tier joint optimization framework based on fractional programming. The numerical results showcase that the HPE performance of the proposed algorithm is nearly optimal, comparable to that of exhaustive search. As a matter of fact, it achieves up to a 120% gain over the benchmark scheme which uses the entire XL-MIMO array with equal power allocation (PA) across sub-arrays, while significantly reducing the computational time.