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Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems

Research output: Contribution to journalArticlepeer-review

Tong Bai, Cunhua Pan, Hong Ren, Yansha Deng, Maged Elkashlan, Arumugam Nallanathan

Original languageEnglish
Article number9388935
Pages (from-to)5389-5407
Number of pages19
Issue number8
Accepted/In press2021
PublishedAug 2021

Bibliographical note

Funding Information: Manuscript received March 17, 2020; revised August 14, 2020 and December 29, 2020; accepted March 7, 2021. Date of publication March 29, 2021; date of current version August 12, 2021. This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1804800 and Grant 2017YFB0503002 and in part by the Engineering and Physical Sciences Research Council under Grant EP/R006466/1. The associate editor coordinating the review of this article and approving it for publication was Y. Cui. (Corresponding author: Cunhua Pan.) Tong Bai is with the School of Cyber Science and Technology, Beihang University, Beijing 100191, China, and also with the School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, U.K. (e-mail: Publisher Copyright: © 2002-2012 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternating optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements.

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