Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with satisfactory user experience. The synchronization between devices in the physical world and their digital models in the metaverse is crucial. This work proposes a sampl...

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Veröffentlicht in:IEEE journal on selected areas in communications Jg. 41; H. 1; S. 288 - 300
Hauptverfasser: Meng, Zhen, She, Changyang, Zhao, Guodong, De Martini, Daniele
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Co-design
Communication
Communications systems
constraint deep reinforcement learning
Constraints
Delays
Horizon
Machine learning
Measurement
Metaverse
prediction
Prediction algorithms
Robot arms
Robot sensing systems
Sampling
Synchronism
Synchronization
Tracking errors
Trajectory
User experience
ISSN:0733-8716, 1558-0008
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Zusammenfassung:The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with satisfactory user experience. The synchronization between devices in the physical world and their digital models in the metaverse is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on the tracking error. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained deep reinforcement learning algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. The results show that our framework achieves a better trade-off between the average tracking error and the average communication load compared with a communication system without sampling and prediction. For example, the average communication load can be reduced up to 87% when the average track error constraint is 0.007°. In addition, our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm achieves better convergence time, stability, communication load, and average tacking error.
Bibliographie:ObjectType-Article-1
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ISSN:0733-8716
1558-0008
DOI:10.1109/JSAC.2022.3221993