Digital twins (DTs) constitute a critical link between the real-world and the metaverse. To guarantee a robust connection between these two worlds, DTs should maintain accurate representations of the physical applications, while preserving synchronization between real and digital entities. In this paper, a novel edge continual learning framework is proposed to accurately model the evolving affinity between a physical twin (PT) and its corresponding cyber twin (CT) while maintaining their utmost synchronization. In particular, a CT is simulated as a deep neural network (DNN) at the wireless network edge to model an autonomous vehicle traversing an episodically dynamic environment. As the vehicular PT updates its driving policy in each episode, the CT is required to concurrently adapt its DNN model to the PT, which gives rise to a de-synchronization gap. Considering the history-aware nature of DTs, the model update process is posed a dual objective optimization problem whose goal is to jointly minimize the loss function over all encountered episodes and the corresponding de-synchronization time. As the de-synchronization time continues to increase over sequential episodes, an elastic weight consolidation (EWC) technique that regularizes the DT history is proposed to limit de-synchronization time. Furthermore, to address the plasticity-stability tradeoff accompanying the progressive growth of the EWC regularization terms, a modified EWC method that considers fair execution between the historical episodes of the DTs is adopted. Ultimately, the proposed framework achieves a simultaneously accurate and synchronous CT model that is robust to catastrophic forgetting. Simulation results show that the proposed solution can achieve an accuracy of 90 % while guaranteeing a minimal desynchronization time.
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学网络空间安全学院）
Tri-Band Channel Measurement-Enabled Multi-Layer Digital Twin for Terahertz Wireless Data Centers
Terahertz Wireless Communications (TWC) Interdisciplinary Research Center, Shanghai Jiao Tong University（上海交通大学太赫兹无线通信（TWC）跨学科研究中心）；Huawei Technologies Company Ltd.（华为技术有限公司）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与工程学院）
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息科学与电子工程学院）；Institute for the Wireless Internet of Things, Northeastern University（东北大学无线互联网研究所）
Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks
Beijing University of Posts and Telecommunications（北京邮电大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
Virginia Tech（弗吉尼亚理工大学）；Ericsson, Inc.（爱立信公司）；Khalifa University of Science and Technology（哈利法科学技术大学）；CentraleSupelec, University Paris-Saclay（巴黎萨克雷大学中央理工-高等电力学院）；Kyung Hee University（庆熙大学）
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
School of Electronic and Optical Engineering, Nanjing University of Science and Technology（南京理工大学电子与光学工程学院）；Department of Computing, Hong Kong Polytechnic University（香港理工大学计算机系）；Data61, CSIRO（数据61，CSIRO）；Department of Electronics Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信研究中心）
Large Vision Model-Enhanced Digital Twin with Deep Reinforcement Learning for User Association and Load Balancing in Dynamic Wireless Networks
National Mobile Communications Research Lab, Southeast University, Nanjing 210096, China（南京东南大学国家移动通信研究中心）；Pervasive Communication Research Center, Purple Mountain Laboratories, Nanjing 211111, China（南京紫金山实验室 pervasive 通信研究中心）
Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin
Department of Civil Engineering and Engineering Mechanics at Columbia University（哥伦比亚大学土木工程与工程力学系）；Data Science Institute（数据科学研究院）；Department of Electrical Engineering at Columbia University（哥伦比亚大学电气工程系）
