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