  In this letter, we investigate an intelligent reflecting surface (IRS) aided
device-to-device (D2D) offloading system, where an IRS is employed to assist in
computation offloading from a group of users with intensive tasks to another
group of idle users. We propose a new two-timescale joint passive beamforming
and resource allocation algorithm based on stochastic successive convex
approximation to minimize the system latency while cutting down the heavy
overhead in exchange of channel state information (CSI). Specifically, the
high-dimensional passive beamforming vector at the IRS is updated in a
frame-based manner based on the channel statistics, where each frame consists
of a number of time slots, while the offloading ratio and user matching
strategy are optimized relied on the low-dimensional real-time effective
channel coefficients in each time slot. The convergence property and the
computational complexity of the proposed algorithm are also examined.
Simulation results show that our proposed algorithm significantly outperforms
the conventional benchmarks.
