D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.

Guan, X; Lv, T; Lin, Z; Huang, P; Zeng, J

D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.

Guan, X Lv, T Lin, Z

Huang, P Zeng, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (18), pp. 7004
Issue Date:: 2022-09-15

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Field	Value	Language
dc.contributor.author	Guan, X
dc.contributor.author	Lv, T
dc.contributor.author	Lin, Z https://orcid.org/0000-0001-5941-2163
dc.contributor.author	Huang, P
dc.contributor.author	Zeng, J
dc.date.accessioned	2023-03-21T02:49:20Z
dc.date.available	2022-09-13
dc.date.available	2023-03-21T02:49:20Z
dc.date.issued	2022-09-15
dc.identifier.citation	Sensors (Basel), 2022, 22, (18), pp. 7004
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/167918
dc.description.abstract	Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22187004
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T02:49:16Z
pubs.issue	18
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	18

Abstract:

Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.

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http://hdl.handle.net/10453/167918