Commonly adopted in the manufacturing and aerospace sectors, digital twin
(DT) platforms are increasingly seen as a promising paradigm to control,
monitor, and analyze software-based, "open", communication systems. Notably, DT
platforms provide a sandbox in which to test artificial intelligence (AI)
solutions for communication systems, potentially reducing the need to collect
data and test algorithms in the field, i.e., on the physical twin (PT). A key
challenge in the deployment of DT systems is to ensure that virtual control
optimization, monitoring, and analysis at the DT are safe and reliable,
avoiding incorrect decisions caused by "model exploitation". To address this
challenge, this paper presents a general Bayesian framework with the aim of
quantifying and accounting for model uncertainty at the DT that is caused by
limitations in the amount and quality of data available at the DT from the PT.
In the proposed framework, 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), monitoring of the PT for anomaly
detection, prediction, data-collection optimization, and counterfactual
analysis. To exemplify the application of the proposed framework, we
specifically investigate a case-study system encompassing multiple sensing
devices that report to a common receiver. Experimental results validate the
effectiveness of the proposed Bayesian framework as compared to standard
frequentist model-based solutions.Comment: Accepted for publication in IEEE Journal on Selected Areas in
Communications ; Extends and subsumes arXiv:2210.05582 ; Updates: -
18/01/2023: Updated reference ; - 29/08/2023: Revised manuscript versio