Space-air-ground integrated networks (SAGINs) provide global information sharing, large-scale coverage, and ubiquitous collaboration architecture, and alleviate the pressure of rapid traffic growth on terrestrial wireless networks. On the other hand, the emergence of artificial intelligence (AI) technology in all walks of life has attracted significant attention on intelligent services. However, frequent link errors and dynamic connections in SAGINs aggravate the issues of data failure and computation slowdown, and constrain the improvement of AI service efficiency. In this article, we propose a novel coded stor-age-and-computation (CSC) architecture, which can offer reliable storage and flexible computation offloading to accelerate distributed machine learning. Through several case studies, we demonstrate that the designed CSC-AI system can realize reliable massive data retrieval and fast computation offloading in SAGINs. Finally, we identify future research issues for CSC-AI system optimization in SAGINs.

中文翻译：

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编码的存储和计算：增强空空地面集成网络中智能服务的新范例

空空地面综合网络（SAGIN）提供全球信息共享，大规模覆盖和无处不在的协作体系结构，并缓解了地面无线网络上流量快速增长的压力。另一方面，各行各业的人工智能（AI）技术的出现引起了人们对智能服务的极大关注。但是，SAGIN中频繁出现的链接错误和动态连接加剧了数据故障和计算速度变慢的问题，并制约了AI服务效率的提高。在本文中，我们提出了一种新颖的编码存储和计算（CSC）架构，该架构可提供可靠的存储和灵活的计算分载，以加速分布式机器学习。通过几个案例研究，我们证明了设计的CSC-AI系统可以在SAGIN中实现可靠的海量数据检索和快速的计算卸载。最后，我们确定了SAGIN中CSC-AI系统优化的未来研究问题。