RSS-Based Channel Estimation for IRS-Aided Wireless Energy Transfer System

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Although intelligent reflecting surface (IRS) is regarded as a promising solution to enhance the efficiency of wireless energy transfer (WET), the acquiring of channel state information is a crucial challenge for the system in which a training sequence for channel estimation is sent by low-power Internet-of-Things (IoT) devices. In this article, an IRS-aided multidevice WET system is considered. To overcome the limitation in channel estimation, we propose a received power-based channel estimation scheme that can be easily implemented and scalable in wirelessly empowered IoT devices. Specifically, at every single time slot, each device measures the received power of a randomly generated radio-frequency signal and feeds it back to the transmitter. We formulate a channel estimation problem to use the history of received power measurements based on the maximum-likelihood estimation using the phase retrieval framework and temporal channel evolution model. Moreover, we propose an algorithm that can be employed to obtain the stationary solution for the channel estimation problem, which is based on the inexact block coordinate descent method. We also perform algorithm modification to deal with the special case in which the transmitter-IRS channel is available. The simulation results show that the performance of the proposed algorithm approaches the upper bound as the channel slowly changes, although the proposed channel estimation protocol requires only one scalar value feedback.

Original languageEnglish
Pages (from-to)14860-14873
Number of pages14
JournalIEEE Internet of Things Journal
Issue number19
Publication statusPublished - 2021 Oct 1

Bibliographical note

Funding Information:
Manuscript received November 20, 2020; revised February 4, 2021 and March 21, 2021; accepted April 2, 2021. Date of publication April 6, 2021; date of current version September 23, 2021. This work was supported by the Samsung Research Funding Center of Samsung Electronics under Project SRFC-IT1701-13. (Corresponding author: Chungyong Lee.) The authors are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:;; Digital Object Identifier 10.1109/JIOT.2021.3071378

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'RSS-Based Channel Estimation for IRS-Aided Wireless Energy Transfer System'. Together they form a unique fingerprint.

Cite this