Contextual-Learning-Based Waveform Scheduling for Wireless Power Transfer With Limited Feedback

Kyeong Won Kim, Hyun Suk Lee, Rui Zhang, Jang Won Lee

Research output: Contribution to journalArticlepeer-review


In this article, we study the waveform scheduling problem for a wireless power transfer (WPT) system consisting of a power beacon (PB) and multiple energy-harvesting-empowered Internet of Things (EH-IoT) devices. In each time slot, each device requests power to the PB if it needs power, and the PB transmits a WPT signal for which the waveform is designed based on the harvested power satisfaction rate of the power-requesting devices. Under this setup, we formulate an optimization problem that maximizes the average number of EH-IoT devices whose power requests are satisfied. We first solve this problem, assuming that the perfect channel state information (CSI) of all devices is known at the PB. Since the problem is difficult to solve even with perfect CSI, we transform it into a more tractable problem via proper approximations and propose an efficient algorithm to solve it. Next, to tackle the issue that it is practically difficult for the PB to acquire the perfect CSI of each device, we propose a contextual learning-based WPT waveform scheduling algorithm, requiring only 1-bit feedback from each device at one time. Numerical results show that our proposed waveform scheduling algorithm provides a higher satisfaction rate than existing algorithms under perfect CSI, and that with limited CSI feedback achieves performance close to the case with perfect CSI.

Original languageEnglish
Pages (from-to)15578-15592
Number of pages15
JournalIEEE Internet of Things Journal
Issue number17
Publication statusPublished - 2022 Sept 1

Bibliographical note

Funding Information:
The work of Kyeong-Won Kim and Jang-Won Lee was supported in part by the National Research Foundation (NRF) Grant funded by the Korea Government (MSIT) under Grant NRF-2019R1A2C2084870. The work of Hyun-Suk Lee was supported by the NRF Grant funded by the Korea Government (MSIT) under Grant NRF-2021R1G1A1004796.

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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