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

arXiv: https://arxiv.org/abs/2208.04233 · HTML: https://mlsanity.com/paper/2208.04233/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 31 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021)
- [Semantics-Empowered Communication for Networked Intelligent Systems](https://mlsanity.com/paper/2007.11579/) (2020)
- [Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning](https://mlsanity.com/paper/1802.06480/) (2018)
- [Prediction and Communication Co-design for Ultra-Reliable and Low-Latency Communications](https://mlsanity.com/paper/1909.05787/) (2019)
- [Realizing the Tactile Internet: Haptic Communications over Next Generation 5G Cellular Networks](https://mlsanity.com/paper/1510.02826/) (2015)
- [Augmented Reality Predictive Displays to Help Mitigate the Effects of Delayed Telesurgery](https://mlsanity.com/paper/1809.08627/) (2018)
- [Joint Sampling and Transmission Policies for Minimizing Cost under AoI Constraints](https://mlsanity.com/paper/2103.15450/) (2021)

## Built on by (most cited first, 7 of 7)

- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends](https://mlsanity.com/paper/2208.10921/) (2022)
- [Optimization of Image Transmission in a Cooperative Semantic Communication Networks](https://mlsanity.com/paper/2301.00433/) (2023)
- [Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning](https://mlsanity.com/paper/2303.04349/) (2023)
- [Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2503.08661/) (2025)
- [Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design](https://mlsanity.com/paper/2508.01745/) (2025)
- [Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation](https://mlsanity.com/paper/2605.19293/) (2026)

## Abstract (arXiv)

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with the help of Mixed Reality (MR) technologies; still, to provide a satisfactory experience for users, the synchronization between the physical world and its digital models is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on tracking the Mean Squared Error (MSE) between a real-world device and its digital model in the metaverse. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained Deep Reinforcement Learning (DRL) algorithm, named Knowledge-assisted Constrained Twin-Delayed Deep Deterministic (KC-TD3) policy gradient algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. Compared with existing approaches: (1) When the tracking error constraint is stringent (MSE=0.002 degrees), our policy degenerates into the policy in the sampling-communication co-design framework. (2) When the tracking error constraint is mild (MSE=0.007 degrees), our policy degenerates into the policy in the prediction-communication co-design framework. (3) Our framework achieves a better trade-off between the average MSE 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 track error constraint is 0.002 degrees. (4) 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 KC-TD3 achieves better convergence time, stability, and final policy performance.