Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems

TL;DR

This paper introduces a novel two-timescale algorithm for IRS-assisted D2D offloading systems that effectively minimizes latency by optimizing passive beamforming and resource allocation with reduced CSI overhead.

Contribution

It proposes a new stochastic successive convex approximation algorithm that jointly optimizes passive beamforming and resource allocation in IRS-assisted D2D systems with reduced CSI overhead.

Findings

Significant latency reduction compared to benchmarks

Effective joint optimization of beamforming and resource allocation

Algorithm convergence and complexity analyzed

Abstract

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.