  An intelligent reflecting surface (IRS)-aided wireless powered mobile edge
computing (WP-MEC) system is conceived, where each device's computational task
can be divided into two parts for local computing and offloading to mobile edge
computing (MEC) servers, respectively. Both time division multiple access
(TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for
uplink (UL) offloading. Given the capability of IRSs in intelligently
reconfiguring wireless channels over time, it is fundamentally unknown which
multiple access scheme is superior for MEC UL offloading. To answer this
question, we first investigate the impact of three different dynamic IRS
beamforming (DIBF) schemes on the computation rate of both offloading schemes,
based on the flexibility for the IRS in adjusting its beamforming (BF) vector
in each transmission frame. Under the DIBF framework, computation rate
maximization problems are formulated for both the NOMA and TDMA schemes,
respectively, by jointly optimizing the IRS passive BF and the resource
allocation. We rigorously prove that offloading adopting TDMA can achieve the
same computation rate as that of NOMA, when all the devices share the same IRS
BF vector during the UL offloading. By contrast, offloading exploiting TDMA
outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL
offloading. Despite the non-convexity of the computation rate maximization
problems for each DIBF scheme associated with highly coupled optimization
variables, we conceive computationally efficient algorithms by invoking
alternating optimization. Our numerical results demonstrate the significant
performance gains achieved by the proposed designs over various benchmark
schemes.
