Efficient Task Offloading for Multi-Access Edge Computing via Intelligent Reflecting Surface

Document Type

Article

Source of Publication

IEEE Communications Magazine

Publication Date

12-11-2025

Abstract

With the widespread and rapid deployment of big data services and technologies like the Internet of Things (IoT) and 5G Advanced, users have become accustomed to applications that demand higher energy consumption and lower latency. As a result, alleviating the computing and communication burdens on user devices, while also addressing concerns related to cost and complexity, has become a major challenge. To tackle these challenges, this article studies the task offloading problem in Intelligent Reflecting Surface (IRS)-assisted Multi-Access Edge Computing (MEC) networks in different scenarios, aiming to enhance the synergy between IRS and MEC. Firstly, we introduce an IRS-assisted MEC network. Secondly, we propose several optimization models for the IRS phase shift coefficient matrix. Finally, we highlight the advantages of the IRS in enhancing MEC communication through a simple experiment.

ISSN

0163-6804

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

64

Issue

1

First Page

108

Last Page

114

Disciplines

Computer Sciences

Keywords

Computer science (0.88), Mobile edge computing (0.65), Software deployment (0.65), Edge computing (0.59), Distributed computing (0.58), Task (project management) (0.57), Energy consumption (0.49), Enhanced Data Rates for GSM Evolution (0.48), Computer network (0.47), Simple (philosophy) (0.45), Big data (0.4), Internet of Things (0.38), The Internet (0.35), Key (lock) (0.35), Server (0.33), Task analysis (0.33), Mobile telephony (0.32), Mobile computing (0.31), Optimization problem (0.31), Cloud computing (0.31), Efficient energy use (0.3), Edge device (0.3), Computation offloading (0.28), Intelligent Network (0.26), Base station (0.26), Communications system (0.25)

Scopus ID

105024794926

Indexed in Scopus

yes

Open Access

no

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