Joint Optimization for Cooperative Computing Framework in Double-IRS-Aided MEC Systems

被引：4

作者：

Zhou, Yi ^{[1
]}

Pan, Cunhua ^{[2
]}

Yeoh, Phee Lep ^{[3
]}

Wang, Kezhi ^{[4
]}

Ma, Zheng ^{[1
]}

Vucetic, Branka ^{[3
]}

Li, Yonghui ^{[3
]}

机构：

[1] Southwest Jiaotong Univ, Key Lab Informat Coding & Transmiss, Chengdu 610031, Peoples R China

[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China

[3] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia

[4] Brunel Univ London, Dept Comp Sci, London UB8 3PH, England

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 05期

基金：

中国国家自然科学基金; 澳大利亚研究理事会;

关键词：

Task analysis; Computational modeling; Resource management; Radio spectrum management; Optimization; Wireless communication; Minimization; Double-IRS; cooperative computing; MEC; LATENCY MINIMIZATION; INTELLIGENT;

D O I：

10.1109/LWC.2023.3243031

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This letter investigates a cooperative task computing framework, where the source node partially offloads its computational task to multiple user equipments (UEs) aided by double intelligent reflecting surfaces (IRSs). With the aim of maximizing the total amount of computing task subject to latency and power constraints, we highlight an interesting tradeoff between the transmit power and the computing power at the source node and optimize the computing frequency resources as well as phase shift matrices for double IRSs. Numerical results verify the power allocation tradeoff and demonstrate the superiority of our double-IRS-aided solution in terms of maximizing the total amount of computing task over other benchmark strategies.

引用

页码：779 / 783

页数：5

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