A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System

TL;DR

This paper introduces a deep learning-based framework called IOPO for rapid, energy-efficient task offloading in IRS-assisted multi-access edge computing systems, addressing the limitations of traditional optimization methods in dynamic wireless environments.

Contribution

The paper presents a novel deep learning framework, IOPO, that significantly accelerates task offloading decisions while maintaining energy efficiency in IRS-assisted MEC systems.

Findings

IOPO generates offloading decisions within milliseconds.

It outperforms benchmark methods in energy efficiency.

It accelerates convergence compared to exhaustive search.

Abstract

Terahertz communication networks and intelligent reflecting surfaces exhibit significant potential in advancing wireless networks, particularly within the domain of aerial-based multi-access edge computing systems. These technologies enable efficient offloading of computational tasks from user electronic devices to Unmanned Aerial Vehicles or local execution. For the generation of high-quality task-offloading allocations, conventional numerical optimization methods often struggle to solve challenging combinatorial optimization problems within the limited channel coherence time, thereby failing to respond quickly to dynamic changes in system conditions. To address this challenge, we propose a deep learning-based optimization framework called Iterative Order-Preserving policy Optimization (IOPO), which enables the generation of energy-efficient task-offloading decisions within milliseconds. Unlike exhaustive search methods, IOPO provides continuous updates to the offloading decisions without resorting to exhaustive search, resulting in accelerated convergence and reduced computational complexity, particularly when dealing with complex problems characterized by extensive solution spaces. Experimental results demonstrate that the proposed framework can generate energy-efficient task-offloading decisions within a very short time period, outperforming other benchmark methods.