Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and
aerial networks with terrestrial wireless systems, are vital enablers of the
emerging sixth-generation (6G) wireless networks. Besides bringing significant
benefits to various applications and services, SAGINs are envisioned to extend
high-speed broadband coverage to remote areas, such as small towns or mining
sites, or areas where terrestrial infrastructure cannot reach, such as
airplanes or maritime use cases. However, due to the limited power and storage
resources, as well as other constraints introduced by the design of terrestrial
networks, SAGINs must be intelligently configured and controlled to satisfy the
envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another
critical enabler of 6G. Due to massive amounts of available data, AI has been
leveraged to address pressing challenges of current and future wireless
networks. By adding AI and facilitating the decision-making and prediction
procedures, SAGINs can effectively adapt to their surrounding environment, thus
enhancing the performance of various metrics. In this work, we aim to
investigate the interplay of AI and SAGINs by providing a holistic overview of
state-of-the-art research in AI-enabled SAGINs. Specifically, we present a
comprehensive overview of some potential applications of AI in SAGINs. We also
cover open issues in employing AI and detail the contributions of SAGINs in the
development of AI. Finally, we highlight some limitations of the existing
research works and outline potential future research directions.

通过综述 AI 在六代无线网络中的应用，全面介绍了 AI 在空中地一体化网络中的潜在应用、AI 使用中存在的问题以及空中地一体化网络在 AI 发展中的贡献，并强调了现有研究的局限性和未来的研究方向。

人工智能与空气 - 地面 - 空间综合网络的相互作用：一项调查

On the Interplay of Artificial Intelligence and Space-Air-Ground  Integrated Networks: A Survey

This letter proposes a novel anti-interference technique, semantic
interference cancellation (SemantIC), for enhancing information quality towards
the sixth-generation (6G) wireless networks. SemantIC only requires the
receiver to concatenate the channel decoder with a semantic auto-encoder. This
constructs a turbo loop which iteratively and alternately eliminates noise in
the signal domain and the semantic domain. From the viewpoint of network
information theory, the neural network of the semantic auto-encoder stores side
information by training, and provides side information in iterative decoding,
as an implementation of the Wyner-Ziv theorem. Simulation results verify the
performance improvement by SemantIC without extra channel resource cost.

该论文提出了一种新颖的抗干扰技术 —— 语义干扰消除（SemantIC），用于提高第六代无线网络的信息质量。SemantIC 只需要接收器将信道译码器与语义自编码器级联，从网络信息理论的角度来看，语义自编码器的神经网络通过训练存储副信息，并在迭代解码中提供副信息，作为 Wyner-Ziv 定理的一种实现。仿真结果验证了 SemantIC 的性能改进，而不需要额外的信道资源成本。