Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluations of our proposed models in a real 5G testbed and benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN--based 5G network by OpenAirInterface (OAI) software at EURECOM. It demonstrated outperforming results against the state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2-4 meters in 90% of cases, across a range of NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper's evaluation.

翻译：信道制图（CC）已成为数据驱动无线电定位领域的一个前景广阔的框架，然而现有方法通常难以实现全局扩展，且难以处理非视距（NLoS）条件引入的失真。本研究提出了一种新颖的CC方法，该方法利用富含实际特征（如到达时间差（TDoA）和收发点（TRP）位置）的信道冲激响应（CIR）数据，实现了在全局尺度上的自监督定位功能。该框架进一步通过短间隔用户设备（UE）位移测量得到增强，从而提升了学习到的定位函数的连续性和鲁棒性。我们的算法包含一种识别并屏蔽由NLoS引起的噪声测量的机制，这带来了显著的性能提升。我们在一个真实的5G测试平台中评估了所提出的模型，并在EURECOM基于OpenAirInterface（OAI）软件的O-RAN 5G网络中，以厘米级精度的实时动态（RTK）定位为基准进行了比较。结果表明，在真实场景中，我们的方法优于当前最先进的半监督和自监督CC方法。结果显示，在90%的情况下，定位精度达到2-4米，且适用于多种NLoS比例。此外，我们公开了本文评估中使用的CIR记录数据集以及真实位置标签。