With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, Edge Intelligence has been envisioned as one of the enabling technologies for ubiquitous and latency-sensitive machine learning based services in future wireless systems. However, with the ever growing scale of the edge-cloud systems as well as the rapid advances in future 6G technologies, there exists a critical issue that limits the deep penetration of future 6G enabled Edge Intelligence services, i.e., how to accurately and reliably evaluate the performance when leveraging the emerging yet pre-matured technologies, protocols, and algorithms into the existing systems. In other words, a fault-tolerant and low-cost platform is necessitated for accurately simulating and evaluating these emerging technologies, without causing any negative influence on the existing systems.

The recent advanced Digital Twin (DT) has been envisioned to provide a promising solution to address this issue. The essence of Digital Twin aims at enabling a systematic and fully digitalized modeling systems that can produce accurate digital model of the corresponding physical identity in the DT-space. By simulating the digital models in the DT-space, Digital Twin is able to accurately and reliably predict and estimate the dynamics and evolutions of the physical networks during the entire life cycle, thus providing a timely and risk-free methodology for evaluating the performance when adopting the emerging yet pre-matured technologies, protocols, and algorithms for network management. Therefore, Digital Twin has been considered as an efficient scheme for addressing the challenging issue to 6G enabled Edge Intelligence, and thus has attracted lots of interests from both academia and industries. It is observed that Digital Twin and 6G enabled Edge Intelligence can benefit each other, which thus yields a deep convergence of the two important technologies.

On the one hand, the increasing network scale, complexity, and security risks of edge intelligence raise more challenges to the security and reliability. Digital Twin can accurately simulate and predict the performance of edge intelligence services in real time, and thus provide accurate benchmark references for 6G Edge Intelligence services. On the other hand, to enable accurate mapping and real-time synchronization between the physical identifies and their digital models, Digital Twin necessitates massive sensing of the targeted physical identifies as well as the consequent data analytics and modeling in real-time. Moreover, efficient yet low-latency transmissions of the sensing data and the data analytics (e.g., the inference models) are required. 6G Edge Intelligence can naturally provide a solution to these requirements.

 

● Multi-sensing, model representation and symbiotic evolution of Digital Twin for 6G enabled Edge Intelligence

● Digital Twin enabled architectures and protocols for 6G enabled Edge Intelligence

● Convergence of Digital Twin and Edge Intelligence under 6G heterogeneous network architectures (e.g., space-air-ground integrated systems)

● Performance evaluation and optimization for convergence of Digital Twin and 6G enabled Edge Intelligence

● Green and low-carbon technologies for convergence of Digital Twin and 6G enabled Edge Intelligence

● Resource management and task scheduling for convergence of Digital Twin and 6G enabled Edge Intelligence

● Security and privacy for convergence of Digital Twin and 6G enabled Edge Intelligence

● Incentive mechanism and economics for convergence of Digital Twin and 6G enabled Edge Intelligence

● Standards, prototypes, and applications for convergence of Digital Twin and 6G enabled Edge Intelligence

 

Submission Deadline: August 10, 2022

Acceptance Notification (1st round): September 25, 2022

Minor Revision Due: October 30, 2022

Final Decision Due: November 30, 2022

Final Manuscript Due: December 25, 2022

Publication Date: March 15, 2023

 

Yuan Wu, University of Macau, China

Xu Chen, Sun Yat-Sen University, China

Feng Lyu, Central South University, China

Xianfu Chen, VTT Technical Research Centre of Finland, Finland

Xumin Huang, Guandong University of Technology, China

Jie Gao, Marquette University, USA

Yueyue Dai, Huazhong University of Science and Technology, China

Tony Q.S. Quek, Singapore University of Technology and Design, Singapore

Yan Zhang, University of Oslo, Norway

 

This feature topic “Convergence of Digital Twin and 6G enabled Edge Intelligence: Theory, Algorithms and Applications” seeks for original, UNPUBLISHED research papers reporting substantive new work in various aspects of topics above. Papers MUST clearly indicate your contributions to the topic field and properly cite related work in this field.

 

Papers should be submitted in two separate .doc files (preferred) or .pdf files: 1) Main Document (including paper title, abstract, key words, and full text); 2) Title page (including paper title, author affiliation, acknowledgement and any other information related with the authors’ identification) through the Manuscript Central. Please register or login at http://mc03.manuscriptcentral.com/chinacomm, then go to the author center and follow the instructions there. Remember to select “ Convergence of Digital Twin and 6G enabled Edge Intelligence: Theories, Algorithms and Applications--- March Issue 2023” as your manuscript type when submitting; otherwise, it might be considered as a regular paper.

 

Each submission must be accompanied by the following information:

● an abstract of no more than 150 words

● 3-8 keywords

● original photographs with high-resolution (300 dpi or greater); eps. ortif. format is preferred; sequentially numbered references.

● sequentially numbered references. The basic reference format is: author name, “article name”, issue name (italic), vol., no., page, month, year. for example: Y. M. Huang, “peradventure in wireless heterogeneous…”, IEEE Journal on Selected Areas, vol. 27, no. 5, pp 34-50, May, 2009.

● brief biographies of authors (50-75 words)

● contact information, including email and mailing addresses