Deep-Reinforcement-Learning-Based Cybertwin Architecture for 6G IIoT: An Integrated Design of Control, Communication, and Computing

Hansong Xu, Jun Wu, Jianhua Li, Xi Lin

研究成果査読

抄録

The cybertwin and 6G-enabled Industrial Internet of Things (6G-IIoT) are the critical technologies that create the digital counterparts for physical systems and enable the near-instant interconnectivity in the industrial domain. It is in demand but challenging to conduct the integrated design for 6G-IIoT, which intertwines the cyber subsystems, such as control, communication, computing (3C), and the physical industrial factories and plants. Therefore, the cybertwin, which synchronizes between the digital counterparts and its physical entities during the system runtime, is the ideal proving ground for conducting the integrated design on the highly intertwined 3C of 6G-IIoT. However, the cybertwin lacks artificial intelligence to capacitate the automated integrated design for the 6G-IIoT. In this article, we first demonstrate the architecture of the machine-learning-based cybertwin for 6G-IIoT. Then, we leverage deep reinforcement learning (DRL) to conduct the integrated design via systematic trial and error in the cybertwin model, which is otherwise costly and dangerous in real industrial systems. Moreover, we invent the adaptive observation window for deep Q -network (AOW-DQN), which generates system states adaptive to the control system's physical dynamics. Finally, the experimental results demonstrate the effectiveness and efficiency of our approach. To the best of our knowledge, we are the first to present the machine-learning-based cybertwin for carrying out the integrated design on the 3C for 6G-IIoT.

本文言語English
ページ(範囲)16337-16348
ページ数12
ジャーナルIEEE Internet of Things Journal
8
22
DOI
出版ステータスPublished - 2021 11 15
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • 情報システム
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

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