Utility Maximization for the IRS-Assisted Wireless Powered Communication Network with Multiple Service Providers

We consider a multi-device wireless-powered communication network, where an intelligent reflecting surface (IRS) is deployed to assist the wireless energy transfer (WET) from the power station (PS) to the information transmitter (IT) and the wireless information transfer (WIT) from the IT to the devices. The IRS, IT, and PS belong to different service providers, where the PS receives revenue from the IT for WET and pays fees to its energy source and the IRS, and the IT receives revenue from the devices for WIT and pays fees to the PS and the IRS. We model the interactions between the IT and the PS through a Stackelberg game and aim to achieve a win-win situation between them in terms of utility. Specifically, we solve a follower problem that maximizes the utility of the PS by jointly optimizing the IRS reflection coefficients and the transmit power of the PS, and a leader problem that maximizes the utility of the IT by jointly optimizing the energy price, the transmit power allocation of the IT, and the time allocation as well as the IRS reflection coefficients in the WET and WIT phases. The results show that, by deploying IRS, it is possible to effectively enhance the signal strength and thus increase the revenue of the system, and by using Time Division Multiple Access (TDMA), the performance of the system is greatly improved by coordinating the timing of the reception of the signal by each device.

Authors

Institutions

Publication Details

Journal
Electronics
Published
2024-08-23
DOI
https://doi.org/10.3390/electronics13173352
Citations
1
Primary Topic
Energy Harvesting in Wireless Networks
Type
article
Field-Weighted Citation Impact
0.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utility Maximization for the IRS-Assisted Wireless Powered Communication Network with Multiple Service Providers

Guangchi Zhang, K. L. Li, Miao Cui, Xinge Sun
1 citations
Electronics
Energy Harvesting in Wireless Networks
0.21
article

Utility Maximization for the IRS-Assisted Wireless Powered Communication Network with Multiple Service Providers

Guangchi Zhang, K. L. Li, Miao Cui, Xinge Sun
article en
1 citations

Abstract

We consider a multi-device wireless-powered communication network, where an intelligent reflecting surface (IRS) is deployed to assist the wireless energy transfer (WET) from the power station (PS) to the information transmitter (IT) and the wireless information transfer (WIT) from the IT to the devices. The IRS, IT, and PS belong to different service providers, where the PS receives revenue from the IT for WET and pays fees to its energy source and the IRS, and the IT receives revenue from the devices for WIT and pays fees to the PS and the IRS. We model the interactions between the IT and the PS through a Stackelberg game and aim to achieve a win-win situation between them in terms of utility. Specifically, we solve a follower problem that maximizes the utility of the PS by jointly optimizing the IRS reflection coefficients and the transmit power of the PS, and a leader problem that maximizes the utility of the IT by jointly optimizing the energy price, the transmit power allocation of the IT, and the time allocation as well as the IRS reflection coefficients in the WET and WIT phases. The results show that, by deploying IRS, it is possible to effectively enhance the signal strength and thus increase the revenue of the system, and by using Time Division Multiple Access (TDMA), the performance of the system is greatly improved by coordinating the timing of the reception of the signal by each device.

ElectronicsVol. 13(17)
Guangdong University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 49%
Energy Harvesting in Wireless Networks
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.