Toward Intelligent SAGIN: Leveraging Big AI Models and SDN for End-to-End Automation
Chenyu Wu, Xi Wang, Yi Hu, Shuai Han, Weixiao Meng, Dusit Tao Niyato · IEEE Network · 2025
Space-air-ground integrated network (SAGIN) is envisioned as a key network architecture for achieving ubiquitous coverage in the next-generation communication system. To manage SAGIN’s inherent complexity, artificial intelligence (AI) provides essential control capabilities, driving enhanced 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.