AI-Empowered LEO Satellite Communications and Networking: Challenges, Enabling Technologies, and Future Applications

Abstract

Low earth orbit (LEO) satellite networks are entering a critical phase of large-scale global deployment. However, their high dynamics pose unprecedented challenges to traditional terrestrial mobile communication and networking protocols. Although artificial intelligence (AI) technology provides new solution paths for high-efficiency and low-complexity operations, it still faces severe technical bottlenecks in practical application and deployment. This paper systematically analyzes three key challenges during the intelligent evolution of LEO satellites: model generalization issue, constrained payload capabilities, and decision latency bottleneck. In response to these challenges, this paper explores potential enabling AI technologies, including the use of meta-learning and graph neural networks to enhance model generalization, the implementation of model compression and lightweight strategies, and the application of generative AI and delay-tolerant reinforcement learning to improve resource management efficiency and decision robustness. On this basis, a further outlook on typical AI-enabled application scenarios is provided: at the communication transmission level, the paper highlights generative AI-driven channel estimation, delay-tolerant beam management, and interference suppression techniques based on diffusion models; at the networking level, graph-based routing strategies and meta-learning-based handover management schemes are discussed. The deep integration of AI technology and satellite communication regimes will serve as a critical support for constructing autonomous and ubiquitously intelligent integrated space information systems in the 6G era.