Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-of-the-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

翻译：卫星通信提供了在未发现和覆盖不足的领域、服务无处不在和服务可扩展性方面保持服务连续性的前景,然而,必须首先应对若干挑战才能实现这些效益,因为卫星网络的资源管理、网络控制、网络安全、频谱管理和能源使用比地面网络更具挑战性;与此同时,人工智能(AI),包括机器学习、深层学习和强化学习,作为一个研究领域,一直在稳步增长,在包括无线通信在内的各种应用方面取得了成功结果;特别是,将AI应用于广泛的卫星通信方面,显示出了极佳的潜力,包括横切、反干扰、网络交通预报、频道建模、遥测采矿、电离层闪烁探测、干扰管理、遥感、行为建模、空间空空地整合和能源管理;因此,这项工作对AI、其各种子领域及其最新算法进行了总体概述;随后讨论了卫星通信系统不同方面所面临的若干挑战,并提出了以AI为基础的解决办法。最后,提出了实地展望,并提出了今后的步骤。