Generative AI for Space-Air-Ground Integrated Networks
Ruichen Zhang, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Ping Zhang, Dong In Kim
Abstract
Recently, generative AI technologies have emerged as significant advancements in the artificial intelligence field, renowned for their language and image generation capabilities. Meantime, the space-air-ground integrated network (SAGIN) is an integral part of future B5G/6G for achieving ubiquitous connectivity. Inspired by this, this article explores an integration of generative AI in SAGIN, focusing on potential applications and a case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities for their integration. Benefiting from generative AI's ability to generate useful data and facilitate advanced decision-making processes, it can be applied to various scenarios of SAGIN. Accordingly, we present a brief survey on their integration, including channel modeling and channel state information (CSI) estimation, joint air-space-ground resource allocation, intelligent network deployment, semantic communications, image extraction and processing, and security and privacy enhancement. Next, we propose a framework that utilizes a generative diffusion model (GDM) to construct a channel information map to enhance quality of service for SAGIN. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we discuss potential research directions for generative AI-enabled SAGIN.