Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface

Adaptive wireless-powered network based on CNN near-field positioning by a dual-band metasurface

Blog Article

Abstract With the womanizer wave 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 spyderco urban 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.