A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems

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 that are expected to dominate 6G deployments. 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.

I. Introduction

A digital twin (DT) platform is a cyberphysical system in which a physical entity, referred to as the physical twin (PT), and a virtual model, known as the DT, interact based on an automatized bi-directional flow of information [1], [2]. Leveraging data received from the PT, the DT maintains an up-to-date model of the PT [3], which is used to control, monitor, and analyze the operation of the PT [4]. DT platforms are increasingly regarded as an enabling technology for wireless cellular systems built on the open networking principles of disaggregation and virtualization [5], which are expected to be central to 6G [6]. Notably, through the available PT model, DT platforms provide a sandbox in which to test algorithms, protocols, and artificial intelligence (AI) solutions for communication systems, potentially reducing the need to collect data and carry out testing in the field, i.e., directly on the PT [4], [7].