The trend of using larger scale antenna arrays will continue toward 6G systems, where the number of antennas will be further scaled up to improve spectral efficiency. However, the increase in the number of antennas will bring new challenges to the physical layer, such as frequent feedback in high-speed mobile communications, multiband coexistence overhead from sub-6 (gigahertz) GHz to terahertz (THz), and energy consumption due to increased antenna components and circuits. In this article, we introduce artificial intelligence (AI)-based channel extrapolation to address these problems. Specifically, we divide the channel extrapolation into time, frequency, and space domains according to different application scenarios. The channel propagation characteristics that affect the extrapolation of each domain, such as the spatial consistency property (SCP), partial reciprocity, and spatial nonstationarity, are analyzed. The motivations for selecting various AI models in each domain are explained, and the performance of AI models is compared. Furthermore, we find the gain of cross-domain channel extrapolation based on transfer learning (TL). The simulation results show that the experience of the AI model cross different domains can be mutually reinforcing. Finally, we introduce several challenges for AI-based channel extrapolation, which can be regarded as potential research directions for realizing future AI-powered 6G systems.

中文翻译：

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面向 6G 的基于 AI 的时域、频域和空域信道外推：机遇与挑战

使用更大规模天线阵列的趋势将继续向 6G 系统发展，其中天线数量将进一步扩大以提高频谱效率。然而，天线数量的增加会给物理层带来新的挑战，例如高速移动通信中的频繁反馈、从 sub-6（千兆赫兹）GHz 到太赫兹（THz）的多频段共存开销，以及由于增加天线元件和电路。在本文中，我们介绍了基于人工智能 (AI) 的信道外推来解决这些问题。具体来说，我们根据不同的应用场景将信道外推分为时域、频域和空域。影响每个域外推的信道传播特性，分析了空间一致性属性 (SCP)、部分互易性和空间非平稳性等。解释了在每个领域选择各种 AI 模型的动机，并比较了 AI 模型的性能。此外，我们发现了基于迁移学习 (TL) 的跨域通道外推的增益。仿真结果表明，AI模型跨不同领域的体验是可以相辅相成的。最后，我们介绍了基于 AI 的信道外推的几个挑战，这些挑战可以被视为实现未来 AI 驱动的 6G 系统的潜在研究方向。我们发现了基于迁移学习 (TL) 的跨域通道外推的增益。仿真结果表明，AI模型跨不同领域的体验是可以相辅相成的。最后，我们介绍了基于 AI 的信道外推的几个挑战，这些挑战可以被视为实现未来 AI 驱动的 6G 系统的潜在研究方向。我们发现了基于迁移学习 (TL) 的跨域通道外推的增益。仿真结果表明，AI模型跨不同领域的体验是可以相辅相成的。最后，我们介绍了基于 AI 的信道外推的几个挑战，这些挑战可以被视为实现未来 AI 驱动的 6G 系统的潜在研究方向。