Latency Minimization for IRS-enhanced Wideband MEC Networks with Practical Reflection Model

TL;DR

This paper proposes a joint optimization framework for IRS-enhanced wideband MEC systems using a practical reflection model, significantly reducing latency compared to ideal models.

Contribution

It introduces a practical IRS reflection model for wideband MEC, and develops an algorithm to jointly optimize offloading, resources, and beamforming for latency minimization.

Findings

Proposed algorithm converges effectively.

Achieves lower latency than ideal IRS models.

Highlights importance of practical IRS modeling.

Abstract

Intelligent reflecting surface (IRS) has been considered as an efficient way to boost the computation capability of mobile edge computing (MEC) system, especially when the communication links is blocked or the communication signal is weak. However, most existing works are restricted to narrow-band channel and ideal IRS reflection model, which is not practical and may lead to significant performance degradation in realistic systems. To further exploit the benefits of IRS in MEC system, we consider an IRS-enhanced wideband MEC system with practical IRS reflection model. With the aim of minimizing the weighted latency of all devices, the offloading data volume, edge computing resource, BS's receiving vector, and IRS passive beamforming are jointly optimized. Since the formulated problem is non-convex, we employ the block coordinate descent (BCD) technique to decouple it into two subproblems for alternatively optimizing computing and communication settings. The effectiveness and convergence of the proposed algorithm are validate via numerical analyses. In addition, simulation results demonstrate that the proposed algorithm can achieve lower latency compared to that based on the ideal IRS reflection model, which confirms the necessary of considering practical model when designing an IRS-enhanced wideband MEC system.