Machine and Deep Learning for Digital Twin Networks: A Survey
Baolin Qin, Heng Pan, Yueyue Dai, Xueming Si, Xiaoyan Huang, Chau Yuen, Yan Zhang
Abstract
Digital twin (DT) is a technology that precisely replicates physical entities and seamlessly connects physical entities with virtual counterparts, which facilitates precise understanding, optimization, and decision-making. DT network (DTN) can be regarded as an information-sharing network, comprising a constellation of interconnected DT nodes. This survey provides an in-depth exploration of the concepts and potential of DTN, with a particular focus on the role of machine and deep learning in improving the efficiency of DTN systems, including anomaly monitoring, system state estimation, resource allocation, task offloading, model optimization, and security and privacy protection. Incorporating machine and deep learning into DTN stands to revolutionize industries by enabling the extraction of critical insights, enhancing anomaly detection capabilities, refining the accuracy of predictive models, and optimizing the allocation of resources. Finally, we discuss the challenges and future research directions in the application of machine and deep learning in DTN.