A Robust Fairness Resource Allocation Algorithm for SWIPT-Enabled Internet of Things with IRS

LI Shidang; CHEN Jin; CHEN Yingying; LONG Chunmei; XU Jinsong; LI Chunguo

doi:10.11999/JEIT221509

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 129-137

LI Shidang, CHEN Jin, CHEN Yingying, LONG Chunmei, XU Jinsong, LI Chunguo. A Robust Fairness Resource Allocation Algorithm for SWIPT-Enabled Internet of Things with IRS[J]. Journal of Electronics & Information Technology, 2024, 46(1): 129-137. doi: 10.11999/JEIT221509

Citation:

LI Shidang, CHEN Jin, CHEN Yingying, LONG Chunmei, XU Jinsong, LI Chunguo. A Robust Fairness Resource Allocation Algorithm for SWIPT-Enabled Internet of Things with IRS[J]. Journal of Electronics & Information Technology, 2024, 46(1): 129-137. doi: 10.11999/JEIT221509

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A Robust Fairness Resource Allocation Algorithm for SWIPT-Enabled Internet of Things with IRS

doi: 10.11999/JEIT221509

1.
School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221116, China
2.
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
3.
Sino-Russian Institute, Jiangsu Normal University, Xuzhou 221116, China
4.
State Key Laboratory of Mobile Communication, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62171119), The National Key R&D Program of China (2017YFF0205500), The Key Research and Development Plan of Xuzhou (KC20027, KC18079), The Chunhui Plan International Cooperation Project of China Education Ministry (202201406, HZKY20220171)

Received Date: 2022-12-06
Rev Recd Date: 2023-05-02

Available Online: 2023-05-19

Publish Date: 2024-01-17

Abstract

Abstract

Focusing on examine the influence of channel errors and the fairness of energy collected by users in 6G internet of things, the problem of maximizing fairness energy for Intelligent Reflecting Surface (IRS)-aided Simultaneous Wireless Information and Power Transfer (SWIPT) is examined when the users have a limited signal-to-interference noise ratio, a transmission power constraint and a reflection phase mode one constraint. As part of the process of solving the nonconvex problem, Schur Complete and S-Process are used to convert the infinite dimensional constraint into a linear inequality involving a finite dimensional matrix, and then the original difficult-to-solve problem is transformed into a standard convex optimization problem using the penalty function and continuous convex approximation, and then an iterative robust fairness energy acquisition algorithm is proposed. Numerical results indicate that the proposed robust optimization algorithm improves the fairness of network harvested energy significantly compared to previous algorithms.

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References(25)

References

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