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Wireless powered mobile edge computing (WP-MEC) has been recognized as apromising technique to provide both enhanced computational capability andsustainable energy supply to massive low-power wireless devices. However, itsenergy consumption becomes substantial, when the transmission link used forwireless energy transfer (WET) and for computation offloading is hostile. Tomitigate this hindrance, we propose to employ the emerging technique ofintelligent reflecting surface (IRS) in WP-MEC systems, which is capable ofproviding an additional link both for WET and for computation offloading.Specifically, we consider a multi-user scenario where both the WET and thecomputation offloading are based on orthogonal frequency-division multiplexing(OFDM) systems. Built on this model, an innovative framework is developed tominimize the energy consumption of the IRS-aided WP-MEC network, by optimizingthe power allocation of the WET signals, the local computing frequencies ofwireless devices, both the sub-band-device association and the power allocationused for computation offloading, as well as the IRS reflection coefficients.The major challenges of this optimization lie in the strong coupling betweenthe settings of WET and of computing as well as the unit-modules constraint onIRS reflection coefficients. To tackle these issues, the technique ofalternative optimization is invoked for decoupling the WET and computingdesigns, while two sets of locally optimal IRS reflection coefficients areprovided for WET and for computation offloading separately relying on thesuccessive convex approximation method. The numerical results demonstrate thatour proposed scheme is capable of monumentally outperforming the conventionalWP-MEC network without IRSs.

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