Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

Mithun Mukherjee, Vikas Kumar, Suman Kumar, Jaime Lloret, Qi Zhang, Mian Guo

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

Original languageEnglish
Title of host publication2021 IEEE Global Communications Conference (GLOBECOM)
Number of pages6
PublisherIEEE
Publication date2021
ISBN (Electronic)9781728181042
DOIs
Publication statusPublished - 2021
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: 7 Dec 202111 Dec 2021

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/TerritorySpain
CityMadrid
Period07/12/202111/12/2021

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