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

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

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, whic...

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Bibliographic Details
Main Authors: Hyun-Suk Lee, Jang-Won Lee
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep learning
IoT devices
mobility
non-static
policy-gradient
switched-beam
Online Access:https://ieeexplore.ieee.org/document/9256306/

Internet

https://ieeexplore.ieee.org/document/9256306/

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