Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems
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. To minimize the system latency while cutting down the heavy overhead in exchange of channel state information (CSI), we study the joint design of beamforming and resource allocation on mixed timescales. 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. A novel mixed-integer stochastic successive convex approximation (MISSCA) algorithm is proposed to tackle the challenging problem. The convergence property and the computational complexity of the proposed algorithm are then examined. Simulation results show that our proposed algorithm significantly outperforms the conventional benchmarks.