An Artificial Intelligence-Assisted Optimization of Imperceptible Multi-Mode Rectenna
Ha, Trung-Dung, Nie, Xuecong, Akinsolu, Mobayode O., Liu, Bo and Chen, Pai-Yen (2024) An Artificial Intelligence-Assisted Optimization of Imperceptible Multi-Mode Rectenna. IEEE Antennas and Wireless Propagation Letters, 23 (11). ISSN 1536-1225
|
Text
WURO_332926.pdf - Accepted Version Available under License Creative Commons Attribution. Download (873kB) | Preview |
Abstract
In this letter, we propose and experimentally demonstrate compact, low-profile, and optically-transparent antennas for multi-band and multi-range wireless power transfer (WPT) applications. Specifically, we put forward new types of transparent multi-band antennas that can perform the near-field reactive WPT (13.56 MHz), as well as the far-field radiative WPT (980 MHz and 2.45 GHz) within a single device. Further, such an antenna is integrated with compact, frequency-scalable rectifying circuits to form an unseeable multi-mode WPT device. We show that a hybrid inductive (13.56 MHz) and radiative (980 MHz and 2.4 GHz) WPT device can be realized with a modified inverted-F antenna (IFA) structure connected to spiral-coil virtual ground. To meet the stringent design requirements of this unobtrusive multi-band antenna, a state-of-the-art machine learning-assisted global optimization method (parallel surrogate model-assisted hybrid differential evolution for antenna optimization or PSADEA) is exploited for global optimization. We envision that the proposed transparent and flexible WPT and energy harvesting devices can be beneficial for many applications, including ubiquitous wireless charging based on smart windows and glasses, solar-radio frequency (RF) integrated power supply, wearable or textile electronics, and internet-of-things (IoTs).
| Item Type: | Article |
|---|---|
| Keywords: | Transparent antennas, AI-assisted antenna design optimization, antenna synthesis, differential evolution, efficient global optimization, energy harvesting |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 13 Nov 2024 16:26 |
| Last Modified: | 13 Nov 2024 16:26 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/18245 |
Actions (login required)
 |
Edit Item |