  The merge of mobile edge computing (MEC) and millimeter-wave (mmWave)
communications will hopefully enable the fast access for computational
resources, where these two technologies can benefit from each other's
potentials. However, the high susceptibility to blocking in mmWave networks
imposes crucial challenges for further development of mmWave-MEC vision. In
this paper, a novel intelligent reflecting surface (IRS) assisted mmWave-MEC
scheme is proposed to overcome the disruptive effect caused by blockage events.
In this context, we investigate new methods to minimize mobile power for a
multi-user mmWave-MEC system, thus efficiently orchestrating the uplink mobile
power resources for latency-constrained computation offloading. In particular,
the mobile power is optimized by joint design of individual device power,
multi-user detection matrix and passive beamforming. To tackle this issue, we
develop an alternating optimization framework so that the joint optimization
can be decomposed into tractable subproblems. First, we provide closed-form
expressions for the update of powers and multi-user detection vectors in each
iteration step. Then, we reformulate the passive beamforming problem. To cater
for large-scale IRS scenario, we propose two efficient algorithms including
complex circle manifold optimization (CCMO) method and sum-of-inverse
minimization (SIMin) fraction transform based alternating direction method of
multipliers (ADMM) method. Finally, numerical results corroborate the merits of
our proposed IRS assisted mmWave-MEC scheme, and demonstrate the feasibility
and effectiveness of our algorithms.
