论文标题
通过模型驱动的贝叶斯学习，基于数字双胞胎的多重访问优化和监视
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
论文作者
论文摘要
在制造和航空航天扇区中通常采用的数字双胞胎（DT）平台越来越被视为一种有希望的范式，可控制和监视基于软件的“ Open”通信系统，它在物理双胞胎（PT）中扮演着角色。在这项工作中提出的一般框架中，DT建立了通信系统的贝叶斯模型，该模型可以利用该模型，以实现核心DT功能，例如通过多机构增强学习（MARL）控制和对PT监测用于异常检测的功能。我们专门研究了所提出的框架在一个简单的案例研究系统中的应用，其中包含多个向通用接收器报告的多个感应设备。在DT训练的贝叶斯模型具有捕获有关通信系统的认知不确定性的关键优势，例如，关于当前的交通状况，这是由于PT到DT数据传输有限而引起的。与标准的基于模型的解决方案相比，实验结果证明了所提出的贝叶斯框架的有效性。
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.
