PACC：结合物理引导度量学习的传播感知信道制图
PACC: Propagation-Aware Channel Charting with Physics-Guided Metric Learning
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中文总结 AI 辅助
针对现有信道制图方法依赖难获取的位置或伪标签的问题，提出无需位置的PACC框架，其设计适配传播条件的度量，仿真显示性能优于同类方法。
中文摘要 AI 辅助
信道制图已成为一种颇具前景的范式，它将高维信道状态信息映射到低维潜在空间，为无线电环境感知、波束管理等任务提供便利。然而，现有方法往往依赖精确的用户位置或基于时间戳的伪标签，这类信息在隐私敏感场景和快速变化的无线环境中难以获取。为解决这些局限，我们提出PACC（结合物理引导度量学习的传播感知信道制图），这是一种无需位置的框架，可从传播特性中构建信道域监督，无需显式地理信息。具体而言，PACC设计了一种传播感知的不相似性度量，该度量会适配视距和非视距传播条件，从而保留局部邻域关系和无线电环境的固有几何结构。仿真结果表明，在各类传播条件下，PACC的性能始终优于经典降维方法和最先进的基于学习的信道制图方法。
英文摘要
Channel charting has emerged as a promising paradigm that maps high-dimensional channel state information into a low-dimensional latent space, facilitating tasks such as radio environment sensing and beam management. However, existing methods often rely on precise user locations or timestamp-based pseudo-labels, which are difficult to obtain in privacy-sensitive scenarios and rapidly varying wireless environments. To address these limitations, we propose propagation-aware channel charting with physics-guided metric learning (PACC), a location-free framework that constructs channel-domain supervision from propagation characteristics without requiring explicit geographic information. Specifically, PACC designs a propagation-aware dissimilarity metric that adapts to line-of-sight and non-line-of-sight propagation conditions, thereby preserving both local neighborhood relationships and the intrinsic geometry of the radio environment. Simulation results demonstrate that PACC consistently outperforms both classical dimensionality-reduction methods and state-of-the-art learning-based channel-charting approaches under diverse propagation conditions.
