A novel framework combining Large Language Models and Continual Reinforcement Learning to manage the complexity of next-generation networks.

• Addresses orchestration challenges in Space-Air-Ground Integrated Networks (SAGINs) and Semantic Communication
• Uses LLMs to handle complex decision-making in dynamic network environments
• Implements continual learning to adapt to evolving network conditions
• Provides autonomous management for ultra-stringent 6G requirements

This research represents a significant advancement in network engineering by applying AI techniques originally developed for robotics to solve complex orchestration problems in future communication networks.

An Autonomous Network Orchestration Framework Integrating Large Language Models with Continual Reinforcement Learning