English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this article, we study wireless power transfer (WPT) beam scheduling for a system which consists of IoT devices and a power beacon (PB) using switched beamforming. In such a system, the IoT devices have a non-static behavior (e.g., their location and power requests keep changing) in general, which conventional WPT beam scheduling algorithms are not capable of adaptively dealing with. To address the non-static behavior, we propose a procedure of deep neural network (DNN)-based WPT beam scheduling. In the procedure, the power-deficient IoT devices transmit a common pilot signal simultaneously. Then, the PB effectively provides power to them with a DNN-based WPT beam scheduling policy. In the DNN-based policy, an estimation of the non-static behavior from the received pilot signals and an adaptive beam generation considering the estimated non-static behavior are integrated thanks to the powerful representational capability of DNNs. To allow the DNN-based policy to learn the optimal policy, we propose a Deep WPT Beam scheduling policy Gradient (DWBG) algorithm using deep reinforcement learning. Through the simulation, we show that DWBG achieves a close performance to the optimal policy. This demonstrates that our algorithm can be applied for practical WPT IoT systems with non-static IoT devices.

Adaptive Wireless Power Transfer Beam Scheduling for Non-Static IoT Devices Using Deep Reinforcement Learning