用于无线定位的学习驱动信道表示：从信道观测到位置推断
Learning-Driven Channel Representation for Wireless Localization: From Channel Observations to Location Inference
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中文总结 AI 辅助
本文以信道表示为视角，将无线定位建模为“无线观测-信道表示-位置推断”框架，回顾相关技术，涵盖观测形式、特征提取等方法，比较典型方法各方面表现，强调信道表示对定位性能的关键作用，还总结了从实验到实际部署的挑战及看法。
中文摘要 AI 辅助
无线观测捕获了通过与传播环境和空间几何相互作用形成的无线电信号响应。在综合传感与通信中，此类观测已成为超越传统信道估计的高精度定位的重要基础。学习驱动方法可学习信道传播与空间位置之间的隐式关系，从而在复杂信道条件下进行位置推断。然而，有用信息与环境布局、时间动态、硬件差异和系统配置紧密耦合，这使得推断过程模糊不清，并削弱了跨场景的性能一致性。本文将定位过程建模为一个统一的“无线观测-信道表示-位置推断”框架，并以信道表示为组织视角回顾学习驱动的高精度定位技术。该综述涵盖了典型的信道观测形式并分析了它们的物理意义。我们还回顾了信道特征提取和表示学习方法，并根据信道表示的获取、组织、适应和重用对方法进行了总结。在准确性、适用条件、数据要求和泛化性方面对典型方法进行了比较。我们强调，信道表示的质量和可用性对于利用传播信息至关重要，因此在定位性能中起着决定性作用。最后，我们总结了从实验研究转向实际部署的关键挑战，并给出了我们对这些问题的看法。
英文摘要
Wireless observations capture radio signal responses formed through interactions with propagation environments and spatial geometry. In integrated sensing and communication, such observations have become an important basis for high-accuracy localization beyond conventional channel estimation. Learning-driven methods learn implicit relations between channel propagation and spatial position, enabling location inference under complex channel conditions. However, the useful information is tightly coupled with environmental layout, temporal dynamics, hardware differences, and system configurations. This coupling obscures the inference process and weakens performance consistency across scenarios. In this paper, we model the localization process as a unified ``wireless observation--channel representation--location inference'' framework, and review learning-driven high-accuracy localization techniques with channel representations as the organizing view. The survey covers typical channel observation forms and analyzes their physical meanings. We also review channel feature extraction and representation learning methods, and summarize methods according to the acquisition, organization, adaptation, and reuse of channel representations. Typical methods are compared in terms of accuracy, applicable conditions, data requirements, and generalization. We highlight that the quality and usability of channel representations are critical to exploiting propagation information, and thus play a decisive role in localization performance. Finally, we summarize the key challenges in moving from experimental studies to real deployment and present our perspectives on these issues.
