A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems
King’s College London（伦敦国王学院）
Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
备注：
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 version
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.
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South China University of Technology（华南理工大学）；Electric Power Research Institute, China Southern Power Grid（中国南方电网电力科学研究院）；Guangdong Provincial Key Laboratory of Power System Network Security（广东省电力系统网络安全重点实验室）；Southern University of Science and Technology（南方科技大学）；Technical University of Berlin（柏林工业大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Khalifa University（哈利法大学）；West Virginia University（西弗吉尼亚大学）
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School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（信息与通信工程学院，北京邮电大学）；Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong (Shenzhen)（大数据深圳研究院，香港中文大学（深圳））；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
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South China University of Technology（华南理工大学）；Technical University of Berlin（柏林工业大学）；Ningbo University（宁波大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Hong Kong Metropolitan University（香港都会大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；University of New South Wales（新南威尔士大学）
