Generative AI for Space-Air-Ground Integrated Networks

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.