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

TL;DR

This paper proposes using reconfigurable intelligent surfaces to enhance uplink communication in edge computing, aiming to minimize worst-case delay in IoT task offloading through optimized bandwidth allocation.

Contribution

It introduces a RIS-assisted edge computing framework with a novel delay minimization strategy using quadratic programming and semidefinite relaxation.

Findings

Improved uplink transmission rates with RIS.

Effective delay reduction in worst-case scenarios.

Scalability demonstrated across various network configurations.

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.