Abstract
Wireless powered mobile edge computing (WP-MEC) has been recognized as a
promising solution to enhance the computational capability and sustainable
energy supply for low-power wireless devices (WDs). However, when the
communication links between the hybrid access point (HAP) and WDs are hostile,
the energy transfer efficiency and task offloading rate are compromised. To
tackle this problem, we propose to employ multiple intelligent reflecting
surfaces (IRSs) to WP-MEC networks. Based on the practical IRS phase shift
model, we formulate a total computation rate maximization problem by jointly
optimizing downlink/uplink IRSs passive beamforming, downlink energy
beamforming and uplink multi-user detection (MUD) vector at HAPs, task
offloading power and local computing frequency of WDs, and the time slot
allocation. Specifically, we first derive the optimal time allocation for
downlink wireless energy transmission (WET) to IRSs and the corresponding
energy beamforming. Next, with fixed time allocation for the downlink WET to
WDs, the original optimization problem can be divided into two independent
subproblems. For the WD charging subproblem, the optimal IRSs passive
beamforming is derived by utilizing the successive convex approximation (SCA)
method and the penalty-based optimization technique, and for the offloading
computing subproblem, we propose a joint optimization framework based on the
fractional programming (FP) method. Finally, simulation results validate that
our proposed optimization method based on the practical phase shift model can
achieve a higher total computation rate compared to the baseline schemes.