Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique

TANG Lun; LI Zhixuan; WEN Wen; CHENG Zhangchao; CHEN Qianbin

doi:10.11999/JEIT230984

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TANG Lun, LI Zhixuan, WEN Wen, CHENG Zhangchao, CHEN Qianbin. Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230984

Citation:

TANG Lun, LI Zhixuan, WEN Wen, CHENG Zhangchao, CHEN Qianbin. Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230984

Citation:

TANG Lun, LI Zhixuan, WEN Wen, CHENG Zhangchao, CHEN Qianbin. Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230984

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Digital Twin Sensing Information Synchronization Strategy Based on Intelligent Hierarchical Slicing Technique

doi: 10.11999/JEIT230984

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), Sichuan Science and Technology Program (2021YFQ0053)

Received Date: 2023-09-07
Rev Recd Date: 2023-12-12

Available Online: 2023-12-22

Abstract

Abstract

In order to mitigate the problem of inaccurate synchronization sensory information in Digital Twins (DTs) caused by unreliable and delayed transmission in Radio Access Networks (RAN), a sensory information synchronization strategy for DTs based on intelligent hierarchical slicing technology is proposed. The strategy aims to optimize the allocation of wireless resources for slicing and the synchronization of DTs' sensing information in dual time scales, with the goals of maximizing the satisfaction of sensing information and minimizing the costs associated with slicing reconfiguration and DTs' synchronization. Firstly, at large time scales, network slicing is employed to provide isolation for DTs with varying Quality of Service (QoS) and resolve deployment challenges; At small time scales, a more flexible wireless resource allocation is utilized to enhance the adaptability of DTs' sensory information synchronization to dynamic environments. Secondly, in order to optimize the synchronization of DTs' sensory information at different time scales, a two-layer Deep Reinforcement Learning (DRL) framework is introduced to facilitate efficient network resource interaction, and in the framework the lower-layer control algorithm incorporates the Prioritized Experience Replay (PER) mechanism to accelerate convergence speed. Finally, the effectiveness of the proposed strategy is validated through simulation results.
- Digital Twin (DT),
- Network slice,
- Deep Reinforcement Learning (DRL),
- State estimation,
- Resource allocation

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References(15)

References

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