Energy-Efficient Resource Allocation for Backscatter-Assisted Wireless Powered MEC

In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consump...

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Vydáno v:IEEE transactions on vehicular technology Ročník 72; číslo 7; s. 9591 - 9596
Hlavní autoři: Shi, Liqin, Ye, Yinghui, Chu, Xiaoli, Sun, Sumei, Lu, Guangyue
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Backscatter communications
Backscattering
Broadcasting
Computational efficiency
Computing time
Edge computing
Energy consumption
Integer programming
Internet of Things
Iterative algorithms
Iterative methods
Mixed integer
Mobile computing
mobile edge computing
Nodes
Optimization
Resource allocation
Resource management
Servers
Task analysis
Wireless communication
ISSN:0018-9545, 1939-9359
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Shrnutí:In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consumption by jointly optimizing the binary offloading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reflection coefficients of IoT nodes, as well as the MEC server's transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we first obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simplified problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simplified problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority over the baseline schemes in terms of the MEC server's energy consumption.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3246237