On Efficient Topology Management in Service-Oriented 6G Networks: An Edge Video Distribution Case Study
Zied Ennaceur, Mounir Bensalem, Admela Jukan, Claus Keuker, Huanzhuo Wu, Rastin Pries · 2025
Efficient topology management in future 6G networks is a fundamental challenge for dynamic network creation based on location services, where each autonomous sub-network can be tailored to specific application scenarios. This paper studies the performance of a novel topology change management system in a 6G network dynamically organized into autonomous sub-networks. We propose and analyze an algorithm for intelligent prediction of topology changes and compare it with a monitoring-based approach. A case study on edge video distribution, aligned with 3GPP and ETSI MEC (Multi-access Edge Computing) standards, demonstrates the system's practical relevance. The proposed topology change prediction algorithm optimizes and selects the best machine learning models based on the scenario under study. For link change scenario, the results show that ANN demonstrates the best performance in identifying cases with no changes, slightly outperforming random forest and XGBoost. For user mobility scenario, XGBoost is more efficient in learning patterns for topology change prediction. In terms of cost efficiency, our ML-based approach represents a significantly cost-effective alternative to traditional monitoring approaches.