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Transfer Reinforcement Learning Aided Distributed Network Slicing Optimization in Industrial IoT
IEEE Transactions on Industrial Informatics
  • Tainle Mai, Beijing University of Posts and Telecommunications
  • Haipeng Yao, Beijing University of Posts and Telecommunications
  • Ni Zhang, Sixth Research Institute of China Electronic Corporation
  • Wenji Ha, Beijing University of Posts and Telecommunications
  • Dong Guo, Beijing Institute of Technology
  • Mohsen Guizani, Mohamed bin Zayed University of Artificial Intelligence
Document Type

With the growth of the number of Internet of Things (IoT) devices and the emergence of new applications, satisfying distinct QoS in the same physical network becomes more challenging. Recently, with the advance of network functions virtualization and software-defined networking (SDN) technologies, the network slicing technique has emerged as a promising solution. It can divide a physical network into multiple virtual networks, therefore providing different network services. In this article, to meet distinct QoS in industrial IoT, we design a network slicing architecture over the SDN-based long-range wide area network. The SDN controller can dynamically split the network into multiple virtual networks according to different business requirements. On this basis, we proposed a deep deterministic policy gradient (DDPG) based slice optimization algorithm. It enables LoRa gateways to intelligently configure slice parameters (e.g., transmission power and spreading factor) to improve the slice performance in terms of QoS, energy efficiency, and reliability. In addition, to accelerate the training process across multiple LoRa gateways, we leverage the transfer learning framework and design a transfer learning-based multiagent DDPG algorithm. © 2005-2012 IEEE.

Publication Date
  • Industrial Internet of Things (IoT),
  • multiagent reinforcement learning,
  • network slicing,
  • transfer learning,
  • Logic gates,
  • Network slicing,
  • Quality of service,
  • Optimization,
  • Measurement,
  • Network servers,
  • Throughput

IR Deposit Conditions:

OA Version (pathway a): Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Citation Information
T. Mai, H. Yao, N. Zhang, W. Ha, D. Guo and M. Guizani, "Transfer Reinforcement Learning Aided Distributed Network Slicing Optimization in Industrial IoT", IEEE Transactions on Industrial Informatics, v. 18.6, p. 4308-4316, Jun. 2022, doi: 10.1109/TII.2021.3132136