Energy-efficient Optimization Algorithm in Multi-tag Wireless-powered Backscatter Communication Networks

XU Yongjun; YANG Haoke; LI Guojun; CHEN Qianbin

doi:10.11999/JEIT210772

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3492-3498

XU Yongjun, YANG Haoke, LI Guojun, CHEN Qianbin. Energy-efficient Optimization Algorithm in Multi-tag Wireless-powered Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3492-3498. doi: 10.11999/JEIT210772

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XU Yongjun, YANG Haoke, LI Guojun, CHEN Qianbin. Energy-efficient Optimization Algorithm in Multi-tag Wireless-powered Backscatter Communication Networks[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3492-3498. doi: 10.11999/JEIT210772

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Energy-efficient Optimization Algorithm in Multi-tag Wireless-powered Backscatter Communication Networks

doi: 10.11999/JEIT210772

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co. Ltd., Chongqing 400030, China
3.
Laboratory of Beyond LOS Reliable Information Transmission, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61601071, 62071078), The Key Program of the National Natural Science Foundation of China (U21A20448), The National Key Research and Development Program (2019YFC1511300), The Natural Science Foundation of Chongqing (cstc2019jcyj-xfkxX0002)

Received Date: 2021-08-02
Accepted Date: 2022-03-08
Rev Recd Date: 2022-03-04

Available Online: 2022-03-18

Publish Date: 2022-10-19

Abstract

Abstract

To improve the operation cycle and energy utilization of Internet of Things (IoT) nodes, an energy-efficient maximization resource allocation algorithm is proposed for a multi-tag wireless-powered backscatter communication network. Specifically, a resource allocation model is developed to maximize the system energy efficiency under the transmission rate constraints, energy harvesting constraints, and transmit power constraints. The original fractional non-convex problem is transformed into a solvable convex optimization problem by using Dinkelbach's theory, a quadratic transformation method, and the variable substitution method. The globally optimal solutions of the considered problem are obtained by using Lagrange dual theory. Simulation results show that the proposed algorithm has better convergence and energy efficiency.
- Wireless-powered communication,
- Backscatter communication,
- Resource allocation,
- Energy efficiency optimization

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References(21)

References

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