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

TL;DR

This paper introduces a digital twin framework that uses Bayesian modeling to optimize and monitor communication systems, enhancing control and anomaly detection through reinforcement learning and uncertainty quantification.

Contribution

It presents a novel Bayesian digital twin approach for communication system management, integrating multi-agent reinforcement learning and uncertainty modeling.

Findings

Bayesian model outperforms frequentist solutions in accuracy.

Effective anomaly detection in communication systems.

Improved control via reinforcement learning.

Abstract

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.