Xia De Xiao, Han Jia Qi, Mu Ya Jie, Guan Lei, Wang Xin, Ma Xiang Jin, Zhu Li Hao, Lv Tian Guang, Liu Hai Xia, Shi Yan, Li Long, Cui Tie Jun
Key Laboratory of High-Speed Circuit Design and EMC of Ministry of Education, School of Electronic Engineering, Xidian University, Xi'an, China.
Institute of Electromagnetic Space and the State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China.
Nat Commun. 2024 Nov 28;15(1):10358. doi: 10.1038/s41467-024-54800-2.
With the improvement of industry, the connectivity of electronic devices gradually shift from wired to wireless. As a solution for power delivery, the non-contact power transfer holds promising ways to charge for moving terminals, enabling battery-free sensing, processing, and communication. Based on a dual-band metasurface, this study proposes an adaptive wireless-powered network (AWPN) to realize the simultaneous wireless localization and non-contact power supply. It first achieves localization with 3 cm resolution on a single-input single-output (SISO) system, by combining space-time-coding (STC) and convolutional neural network (CNN). With precise position information, AWPN real-time aligns power beams to the terminals for stable energy transmission. Then, battery-free terminals enable to perceive the environmental data and uploads the results. From the measurement results, AWPN gets more than 98% CNN classification accuracy and can tolerate certain environmental changes. Thus, being adaptive and contactless, our study will propel the advancement in Internet of Things (IoT), intelligent metasurface, and the robot industry.
随着工业的发展，电子设备的连接方式逐渐从有线转向无线。作为一种电力传输解决方案，非接触式电力传输为移动终端充电提供了有前景的方式，实现了无需电池的传感、处理和通信。基于双频超表面，本研究提出了一种自适应无线供电网络（AWPN），以实现同时的无线定位和非接触式供电。它首先通过结合空时编码（STC）和卷积神经网络（CNN），在单输入单输出（SISO）系统上实现了3厘米分辨率的定位。有了精确的位置信息，AWPN实时将功率波束对准终端，以实现稳定的能量传输。然后，无需电池的终端能够感知环境数据并上传结果。从测量结果来看，AWPN的CNN分类准确率超过98%，并且能够容忍一定的环境变化。因此，由于具有自适应性和非接触性，我们的研究将推动物联网（IoT）、智能超表面和机器人行业的发展。
