  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.
