Space communications, particularly massive satellite networks, re-emerged as an appealing candidate for next generation networks due to major advances in space launching, electronics, processing power, and miniaturization. However, massive satellite networks rely on numerous underlying and intertwined processes that cannot be truly captured using conventionally used models, due to their dynamic and unique features such as orbital speed, inter-satellite links, short pass time, and satellite footprint, among others. Hence, new approaches are needed to enable the network to proactively adjust to the rapidly varying conditions associated within the link. Artificial intelligence (AI) provides a pathway to capture these processes, analyze their behavior, and model their effect on the network. This article introduces the application of AI techniques for integrated terrestrial satellite networks, particularly massive satellite network communications. It details the unique features of massive satellite networks, and the overarching challenges concomitant with their integration into the current communication infrastructure. Moreover, this article provides insights into state-of-the-art AI techniques across various layers of the communication link. This entails applying AI for forecasting the highly dynamic radio channel, spectrum sensing and classification, signal detection and demodulation, inter-satellite and satellite access network optimization, and network security. Moreover, future paradigms and the mapping of these mechanisms onto practical networks are outlined.

翻译：空间通信，特别是大规模卫星网络，由于太空发射、电子技术、计算能力及微型化方面的重大进展，再次成为下一代网络的有力候选方案。然而，大规模卫星网络依赖于众多底层且相互交织的进程，由于其动态特性及独特特征（如轨道速度、星间链路、短过境时间及卫星覆盖范围等），传统模型难以准确捕捉这些进程。因此，需要新方法使网络能够主动适应链路上快速变化的条件。人工智能（AI）为捕捉这些进程、分析其行为并建模其对网络的影响提供了途径。本文介绍了AI技术在天地一体化卫星网络，特别是大规模卫星网络通信中的应用。文章详细阐述了大尺度卫星网络的独特特征，以及其融入现有通信基础设施时所面临的总体挑战。此外，本文深入探讨了通信链路各层中的前沿AI技术，包括应用AI预测高度动态的无线电信道、频谱感知与分类、信号检测与解调、星间及卫星接入网优化，以及网络安全性。最后，阐述了未来范式及将这些机制映射到实际网络的方案。