Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Meantime, 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 case study. We first provide a comprehensive review of SAGIN and generative AI models, highlighting their capabilities and opportunities of 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 concise 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, security and privacy enhancement. Next, we propose a framework that utilizes a Generative Diffusion Model (GDM) to construct 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.

翻译：近年来，生成式人工智能技术作为人工智能领域的重大突破而兴起，以其语言和图像生成能力著称。同时，空天地一体化网络（SAGIN）是实现未来B5G/6G全域覆盖的关键组成部分。受此启发，本文探索了生成式人工智能在SAGIN中的融合应用，重点关注其潜在应用场景和案例研究。我们首先全面回顾了SAGIN与生成式AI模型，阐述了其各自能力及融合机遇。利用生成式AI生成有效数据并促进高级决策过程的特性，该技术可应用于SAGIN的多种场景。据此，我们对其融合应用进行了简要综述，涵盖信道建模与信道状态信息（CSI）估计、空天地联合资源分配、智能网络部署、语义通信、图像提取与处理、安全与隐私增强等方向。随后，我们提出了一种利用生成扩散模型（GDM）构建信道信息图以提升SAGIN服务质量的框架。仿真结果验证了所提框架的有效性。最后，我们探讨了基于生成式AI的SAGIN未来潜在研究方向。