Hierarchical Digital Twin for Efficient 6G Network Orchestration via Adaptive Attribute Selection and Scalable Network Modeling

Abstract

Achieving a holistic and long-term understanding through accurate network modeling is essential for orchestrating future networks with increasing service diversity and infrastructure complexities. However, due to unselective data collection and uniform processing, traditional modeling approaches undermine the efficacy and timeliness of network orchestration. Additionally, temporal disparities arising from various modeling delays further impair the centralized decision-making with distributed models. In this paper, we propose a new hierarchical digital twin paradigm adapting to real-time network situations for problem-centered model construction. Specifically, we introduce an adaptive attribute selection mechanism that evaluates the distinct modeling values of diverse network attributes, considering their relevance to current network scenarios and inherent modeling complexity. By prioritizing critical attributes at higher layers, an efficient evaluation of network situations is achieved to identify target areas. Subsequently, scalable network modeling facilitates the inclusion of all identified elements at the lower layers, where more fine-grained digital twins are developed to generate targeted solutions for user association and power allocation. Furthermore, virtual-physical domain synchronization is implemented to maintain accurate temporal alignment between the digital twins and their physical counterparts, spanning from the construction to the utilization of the proposed paradigm. Extensive simulations validate the proposed approach, demonstrating its effectiveness in efficiently identifying pressing issues and delivering network orchestration solutions in complex 6G HetNets.