Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing

Sun, C; Ni, W; Bu, Z; Wang, X

Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing

Sun, C Ni, W

Bu, Z Wang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2022, 21, (8), pp. 6329-6344
Issue Date:: 2022-08-01

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Field	Value	Language
dc.contributor.author	Sun, C
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Bu, Z
dc.contributor.author	Wang, X
dc.date.accessioned	2023-04-06T04:47:38Z
dc.date.available	2023-04-06T04:47:38Z
dc.date.issued	2022-08-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2022, 21, (8), pp. 6329-6344
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/169330
dc.description.abstract	Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/TWC.2022.3148296
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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	closed_access	*
dc.date.updated	2023-04-06T04:47:37Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	8

Abstract:

Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.

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http://hdl.handle.net/10453/169330