Distributed Resource Management in Unlicensed Assisted Mobile Edge Computing

Rui Yin; Xiao Lu; Chao Chen; Xianfu Chen; Celimuge Wu

doi:10.1109/JIOT.2023.3302805

Distributed Resource Management in Unlicensed Assisted Mobile Edge Computing

Rui Yin, Xiao Lu, Chao Chen, Xianfu Chen^*, Celimuge Wu

^*Corresponding author for this work

BA6401 Connectivity solutions

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

Abstract

This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

Original language	English
Pages (from-to)	20662-20674
Journal	IEEE Internet of Things Journal
Volume	10
Issue number	23
DOIs	https://doi.org/10.1109/JIOT.2023.3302805
Publication status	Published - 1 Dec 2023
MoE publication type	A1 Journal article-refereed

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62271438, Grant 62111540270, and Grant 62062031; in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LGG22F010008, Grant LZ23F010003, and Grant LQ23F010009; in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant XRK22005; in part by the Zhejiang Provincial Key Laboratory of New Network Standards and Technologies under Grant 2013E10012; in part by the ROIS NII Open Collaborative Research under Grant 23S0601; and in part by JSPS KAKENHI under Grant 20H00592 and Grant 21H03424.

Keywords

Computation offloading
Computational modeling
energy saving
Games
MEC
Resource management
Task analysis
Throughput
unlicensed spectrum
Wireless communication
Wireless fidelity
mobile edge computing (MEC)

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10.1109/JIOT.2023.3302805

Cite this

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title = "Distributed Resource Management in Unlicensed Assisted Mobile Edge Computing",

abstract = "This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.",

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author = "Rui Yin and Xiao Lu and Chao Chen and Xianfu Chen and Celimuge Wu",

note = "Publisher Copyright: 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.",

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AU - Chen, Chao

AU - Chen, Xianfu

AU - Wu, Celimuge

N1 - Publisher Copyright: 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

PY - 2023/12/1

Y1 - 2023/12/1

N2 - This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

AB - This article studies joint power, spectrum and computational resource allocation in mobile edge computing (MEC) systems. Considering that the licensed spectrum resources are not sufficient, the computing tasks can also be uploaded to the remote MEC server (MECS) via the unlicensed spectrum. To facilitate fair coexistence with Wi-Fi networks, we adopt the duty-cycle-muting mechanism with adaptive adjustment of the duty cycle on unlicensed channels. We propose a Stackelberg game formulation, where the aim is to minimize the long-term energy consumption of the noncooperative user terminals (UEs) while guaranteeing the stability of task buffers. In the game, the MECS prices the licensed spectrum to indirectly adjust the proportion of bandwidth for each UE. In particular, we develop a distributed resource management algorithm, which enables the UEs to behave independently and adaptively. Theoretical analysis and simulations demonstrate the effectiveness of our proposed algorithm with respect to energy saving under constrained signaling overheads.

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KW - Wireless communication

KW - Wireless fidelity

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Distributed Resource Management in Unlicensed Assisted Mobile Edge Computing

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