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Digital Twin-Assisted Edge Computation Offloading in Industrial Internet of Things With NOMA
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  • Long Zhang ,
  • Han Wang ,
  • Hongmei Xue ,
  • Hongliang Zhang ,
  • Qilie Liu ,
  • Dusit Niyato ,
  • Zhu Han
Long Zhang
Hebei University of Engineering, Hebei University of Engineering

Corresponding Author:[email protected]

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Hongmei Xue
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Hongliang Zhang
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Qilie Liu
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Dusit Niyato
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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 challenging to solve. We decompose the original problem into four sub-problems, and then propose an efficient iterative algorithm to solve this problem by leveraging the block coordinate descent method. Therefore, the subchannel assignment, computation capacity allocation, edge association, and power control are alternately optimized in an iterative way, and then a locally optimal solution of the original problem is obtained until convergence. Simulation results demonstrate the benefits of the proposed scheme over the other benchmarks in reducing the total task completion delay and increasing the percentage of offloading IIoT devices with low complexity.
Sep 2023Published in IEEE Transactions on Vehicular Technology volume 72 issue 9 on pages 11935-11950. 10.1109/TVT.2023.3270859