Agentic AI for SAGIN Resource Management_Semantic Awareness, Orchestration, and Optimization

2026-03-17

AI Summary

This work addresses the resource management challenges in space-air-ground integrated networks (SAGIN) arising from heterogeneous infrastructure, dynamic topology, and stringent QoS requirements. To this end, the authors propose an intelligent agent framework powered by large language models (LLMs) and embedded within a MAPE-K adaptive control plane. The framework orchestrates three types of agents—semantic-aware, intent-driven, and adaptive learning agents—that collaborate to bridge the semantic gap between high-level operational intent and low-level network execution. A novel hierarchical agent-reinforcement learning mechanism is introduced, wherein LLM-based agents dynamically construct reward functions based on semantically enriched network states. Evaluated in a UAV-assisted AIGC service orchestration scenario, the proposed approach significantly outperforms existing methods, achieving a 14% reduction in energy consumption and the lowest average service latency.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Space-air-ground integrated networks (SAGIN) promise ubiquitous 6G connectivity but face significant resource management challenges due to heterogeneous infrastructure, dynamic topologies, and stringent quality-of-service (QoS) requirements. Conventional model-driven approaches struggle with scalability and adaptability in such complex environments. This paper presents an agentic artificial intelligence (AI) framework for autonomous SAGIN resource management by embedding large language model (LLM)-based agents into a Monitor-Analyze-Plan- Execute-Knowledge (MAPE-K) control plane. The framework incorporates three specialized agents, namely semantic resource perceivers, intent-driven orchestrators, and adaptive learners, that collaborate through natural language reasoning to bridge the gap between operator intents and network execution. A key innovation is the hierarchical agent-reinforcement learning (RL) collaboration mechanism, wherein LLM-based orchestrators dynamically shape reward functions for RL agents based on semantic network conditions. Validation through UAV-assisted AIGC service orchestration in energy-constrained scenarios demonstrates that LLM-driven reward shaping achieves 14% energy reduction and the lowest average service latency among all compared methods. This agentic paradigm offers a scalable pathway toward adaptive, AI-native 6G networks, capable of autonomously interpreting intents and adapting to dynamic environments.

Problem

• SAGIN
• resource management
• 6G networks
• quality-of-service
• dynamic topologies

Innovation

• Agentic AI
• LLM-based orchestration
• Semantic-aware resource management
• Hierarchical RL collaboration
• MAPE-K control plane