AI-native architecture for 6G networks and services with model dependencies

Next generation networks and services increasingly rely on AI/ML methods to integrate intelligence in the decision making process with the introduction of MLOps pipelines for automating orchestration and lifecycle management. Further, Digital Twinning is viewed as a key enabling technology for 6G networks due to its foreseen benefits in network management optimization by providing a digital replica of the network. However, further work is required for an AI-native 6G architecture that fully integrates AI/ML into network and service management. Indeed, the use of Digital Twins and Transfer Learning leads to dependencies between models, which ought to be taken into account during their lifecycle management. Hence, the work described in this paper provides a framework for enabling an AI-native network architecture which supports the handling of AI/ML model interdependencies and relationships in network and service management and orchestration.