Guest editorial: Convergence of digital twin and 6G enabled edge intelligence: Theories, algorithms and applications

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 evaluate the performance when leveraging the emerging yet pre-matured technologies, protocols, and algorithms into the existing systems. The recent advanced Digital Twin (DT) has been envisioned to provide a fault-tolerant and low-cost platform for accurately simulating and evaluating the emerging technologies, protocols, and algorithms, without causing any negative influence on the existing systems. The essence of DT 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, DT can 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, DT 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 DT and edge intelligence can benefit each other, which thus yields a deep convergence of themselves. On the one hand, the increasing network scale, complexity, and security risks of edge intelligence raise more challenges to the security and reliability. DT can accurately simulate and predict the performance of edge intelligence services and thus provide accurate benchmark references for edge services. On the other hand, to enable accurate mapping and real-time synchronization between the physical identifies and their digital models, DT necessitates massive sensing of the targeted physical identifies as well as the consequent data analytics and modeling. 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. Therefore, this special issue focuses on the convergence of DT and 6G enabled Edge Intelligence, from the perspectives of theories, algorithms, and applications.