Digital Twin-Assisted Edge Computation Offloading in Industrial Internet
of Things With NOMA
Abstract
Integrating digital twins (DTs) and multi-access edge computing (MEC) is
a promising technology that realizes edge intelligence in 6G, which has
been recognized as the key enabler for Industrial Internet of Things
(IIoT). In this paper, we explore a DT-assisted MEC system for the IIoT
scenario where a DT server is created as a digital counterpart of the
MEC server, via estimating the computation state of the MEC server
within the DT modelling cycle. To achieve energy and spectrally
efficient offloading, we consider that IIoT devices communicate with
industrial gateways (IGWs) through a non-orthogonal multiple access
(NOMA) protocol. Each IIoT device has an industrial computation task
that can be executed locally or fully offloaded to IGW. We aim to
minimize the total task completion delay of all IIoT devices by jointly
optimizing the IGW’s subchannel assignment as well as the computation
capacity allocation, edge association, and transmit power control of
IIoT device. The resulting problem is shown to be a mixed integer
non-convex optimization problem, which is NP-hard and challenging to
solve. We decompose the original problem into four solvable
sub-problems, and then propose an overall alternating optimization
algorithm to solve the sub-problems iteratively until convergence.
Validated via simulations, the proposed scheme shows superiority to the
benchmarks in reducing the total task completion delay and increasing
the percentage of offloading IIoT devices.