# Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning

arXiv: https://arxiv.org/abs/2210.05582 · HTML: https://mlsanity.com/paper/2210.05582/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 11 Oct 2022.

## Builds on (most cited first, 14 of 14)

- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [On Improving Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1804.06309/) (2018)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection](https://mlsanity.com/paper/1912.05651/) (2019)
- [Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Access](https://mlsanity.com/paper/2007.09948/) (2020)
- [Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks](https://mlsanity.com/paper/2011.08430/) (2020)
- [Edge Continual Learning for Dynamic Digital Twins over Wireless Networks](https://mlsanity.com/paper/2204.04795/) (2022)
- [Centralized Model and Exploration Policy for Multi-Agent RL](https://mlsanity.com/paper/2107.06434/) (2021)
- [Network Digital Twin: Context, Enabling Technologies and Opportunities](https://mlsanity.com/paper/2205.14206/) (2022)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling](https://mlsanity.com/paper/2208.01781/) (2022)
- [Digital Twin for Networking: A Data-driven Performance Modeling Perspective](https://mlsanity.com/paper/2206.00310/) (2022)
- [Learning Generalized Wireless MAC Communication Protocols via Abstraction](https://mlsanity.com/paper/2206.06331/) (2022)
- [Multi-Agent Deep Stochastic Policy Gradient for Event Based Dynamic Spectrum Access](https://mlsanity.com/paper/2004.02656/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)

## Abstract (arXiv)

Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open", communication systems, which play the role of the physical twin (PT). In the general framework presented in this work, the DT builds a Bayesian model of the communication system, which is leveraged to enable core DT functionalities such as control via multi-agent reinforcement learning (MARL) and monitoring of the PT for anomaly detection. We specifically investigate the application of the proposed framework to a simple case-study system encompassing multiple sensing devices that report to a common receiver. The Bayesian model trained at the DT has the key advantage of capturing epistemic uncertainty regarding the communication system, e.g., regarding current traffic conditions, which arise from limited PT-to-DT data transfer. Experimental results validate the effectiveness of the proposed Bayesian framework as compared to standard frequentist model-based solutions.