A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT) is proposed, which enables distributed on-device generation at mobile edge networks for real-time DT applications. First, an MEG-DT architecture is put forward to decentralize generative artificial intelligence (GAI) models onto edge servers (ESs) and user equipments (UEs), which has the advantages of low latency, privacy preservation, and individual-level customization. Then, various single-user and multi-user generation mechanisms are conceived for MEG-DT, which strike trade-offs between generation latency, hardware costs, and device coordination. Furthermore, to perform efficient distributed generation, two operating protocols are explored for transmitting interpretable and latent features between ESs and UEs, namely sketch-based generation and seed-based generation, respectively. Based on the proposed protocols, the convergence between MEG and DT are highlighted. Considering the seed-based image generation scenario, numerical case studies are provided to reveal the superiority of MEG-DT over centralized generation. Finally, promising applications and research opportunities are identified. Code is available at https://github.com/xiaoxiaxusummer/MEG_DT
Digital Twin Enabled Site Specific Channel Precoding: Over the Air CIR Inference
Department of EECS, Howard University（霍华德大学电子工程与计算机科学系）；Department of Electrical and Computer Engineering, Université Laval（拉瓦尔大学电子与计算机工程系）；DeepSig Inc.（DeepSig公司）
Adaptive Digital Twin and Communication-Efficient Federated Learning Network Slicing for 5G-enabled Internet of Things
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Electro-optical Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学光电工程学院）；Technische University（工业大学）
Deep Neural Operator Enabled Digital Twin Modeling for Additive Manufacturing
Global Engineering and Materials, Inc.（全球工程与材料公司）；Applied Research Laboratory, The Pennsylvania State University（宾夕法尼亚州立大学应用研究实验室）；Grado Department of Industrial and Systems Engineering, Virginia Tech（弗吉尼亚理工大学工业与系统工程系）；Structures Division, Naval Air Systems Command (NAVAIR)（海军航空系统司令部结构分部）；Department of Mathematics, Lehigh University（理海大学数学系）
Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface
State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing, Inner Mongolia University（内蒙古大学智能通信与传感及信号处理重点实验室）；Future Research Laboratory, China Mobile Research Institute（中国移动研究院未来实验室）
Metasurfaces-Enabled Wave Computing for Future Wireless Systems: Opportunities and Challenges
Institute for Communication Systems, University of Surrey（萨里大学通信系统研究所）；Universidad de Oviedo（奥维耶多大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚斯大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Centre for Wireless Innovation, Queen’s University Belfast（贝尔法斯特女王大学无线创新中心）
