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

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