  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
alternative 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.
