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.

翻译：数字双星平台日益被视为控制和监测基于软件的“开放”通信系统的有希望的范例,这种系统起着有形双星的作用。在这项工作提出的总框架内,数字双星平台建立了巴伊西亚通信系统模式,利用这一模式使核心的DT功能得以发挥,例如通过多试剂强化学习(MARL)控制以及监测PT以发现异常现象。我们特别调查了将拟议框架应用于一个简单案例研究系统的情况,该系统包括向一个共同接收者报告的多种感测装置。在DT培训的Bayesian模型的主要优势是捕捉到通信系统方面的隐蔽不确定性,例如,目前由于有限的PT-T-DT数据传输而产生的交通条件。实验结果证实,拟议的Bayesian框架与标准的常态模式解决方案相比,取得了成效。