Wang, Yuchao and He, Shi and Qiu, Yongxue and Wu, Ruiyuan and Wang, Lei and Lu, Ping and Song, Chaoyun and Cheng, Qiang and Zhang, Cheng (2024) Highly Efficient Broadband Ambient Energy Harvesting System Enhanced by Meta-Lens for Wirelessly Powering Battery-less IoT Devices. IEEE Internet of Things Journal, 11 (16). 26916 - 26928. ISSN 2327-4662
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Abstract
Existing Internet of Things (IoT) devices face a significant challenge in terms of power consumption due to their limited battery life. Capturing and utilizing ambient radio frequency (RF) energy emerges as a promising solution for powering low-power sensors and electronic devices, given its unique spatial and temporal distributions. However, the low level of ambient RF power severely hampers the rectenna’s RF-to-direct current (DC) conversion efficiency, making it incapable of generating sufficient DC power. To address this issue and enhance the conversion efficiency of a broadband rectenna at low environmental power levels, this study introduces a novel technique called the meta-lens assisted technique (MAT). This technique leads to a substantial increase in the rectenna’s received RF power by more than 10 dB. As a result, the total conversion efficiency improves by over 30% across a wide frequency band ranging from 2.9 GHz to 3.63 GHz (with a fractional bandwidth of 22.3%), even when the initial RF power received (without the MAT) was as low as -20 dBm, which approaches the real-life ambient RF power level. Notably, the proposed MAT achieves a 40% to 60% efficiency improvement compared to state-of-the-art approaches. These remarkable results demonstrate the promising potential of the MAT rectenna as an alternative for harvesting low-density wireless energy and supporting low-power-required industrial IoT applications.