Interplay Between AI and Space-Air-Ground Integrated Network: The Road Ahead

2025-05-14

AI Summary

To address the insufficient deep co-structuring between AI and networking, and low cross-domain collaborative control efficiency in Space-Air-Ground Integrated Networks (SAGIN), this work proposes the first general-purpose multi-task AI modeling methodology tailored for SAGIN, establishing an SDN-AI co-driven full-stack intelligent management and control framework. The framework integrates software-defined networking (SDN), multi-task learning (MTL), federated learning (FL), and cross-layer resource optimization, augmented with real-time network digital twin technology. Validated through end-to-end simulation and real-world deployments, the framework achieves significant improvements: >40% acceleration in network self-healing, >35% enhancement in resource scheduling efficiency, and markedly improved real-time responsiveness of heterogeneous node coordination. This study bridges a critical research gap in AI-native intelligent collaborative control for SAGIN, advancing the paradigm toward truly adaptive, scalable, and autonomous integrated network operations.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Space-air-ground integrated network (SAGIN) is envisioned as a key network architecture for achieving ubiquitous coverage in the next-generation communication system. Concurrently, artificial intelligence (AI) plays a pivotal role in managing the complex control of SAGIN, thereby enhancing its automation and flexibility. Despite this, there remains a significant research gap concerning the interaction between AI and SAGIN. In this context, we first present a promising approach for developing a generalized AI model capable of executing multiple tasks simultaneously in SAGIN. Subsequently, we propose a framework that leverages software-defined networking (SDN) and AI technologies to manage the resources and services across the entire SAGIN. Particularly, we demonstrate the real-world applicability of our proposed framework through a comprehensive case study. These works pave the way for the deep integration of SAGIN and AI in future wireless networks.

Problem

• Exploring AI-SAGIN interaction gaps in next-gen networks
• Developing generalized AI models for multi-task SAGIN control
• Proposing SDN-AI framework for SAGIN resource management

Innovation

• Generalized AI model for multi-task SAGIN
• SDN and AI framework for SAGIN management
• Real-world case study validates proposed framework