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

TL;DR

This paper introduces an agentic AI framework for autonomous resource management in SAGIN, leveraging LLM-based agents and hierarchical RL to improve scalability, adaptability, and efficiency in complex 6G networks.

Contribution

It proposes a novel hierarchical agent-reinforcement learning mechanism with LLM-based agents for semantic-aware, autonomous SAGIN resource management.

Findings

Achieved 14% energy reduction in UAV-assisted service orchestration.

Demonstrated lowest average service latency compared to baseline methods.

Validated the framework's effectiveness in energy-constrained, dynamic environments.

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.