Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hardware and renders label acquisition difficult in large areas. In this work, we propose a novel neural positioning pipeline that avoids the need for any external reference positioning system. Our approach trains the positioning network only using CSI acquired off-device and relative displacement commands executed on commercial off-the-shelf (COTS) robot platforms, such as robotic vacuum cleaners -- such an approach enables inexpensive training of accurate neural positioning functions over large areas. We evaluate our method in three real-world scenarios, ranging from small line-of-sight (LoS) areas to larger non-line-of-sight (NLoS) environments, using CSI measurements acquired in IEEE 802.11 Wi-Fi and 5G New Radio (NR) systems. Our experiments demonstrate that the proposed neural positioning pipeline achieves UE localization accuracies close to state-of-the-art methods that require externally acquired high-precision ground-truth position labels for training.

翻译：基于信道状态信息（CSI）的神经网络用户设备（UE）定位——即神经定位——是实现高精度非设备端UE定位的一种有前景的方法。现有方法大多依赖从外部参考定位系统获取的真实位置标签来训练神经网络，这需要昂贵的硬件支持，且在大范围区域内难以获取标签。本研究提出一种新型神经定位流程，完全无需外部参考定位系统。该方法仅利用非设备端采集的CSI数据，结合商用现成（COTS）机器人平台（如扫地机器人）执行的相对位移指令来训练定位网络——这种方案能够以低成本在大范围区域内训练出精确的神经定位函数。我们在三种真实场景中评估了该方法，涵盖小范围视距（LoS）区域至大范围非视距（NLoS）环境，所使用的CSI测量数据采集自IEEE 802.11 Wi-Fi及5G新空口（NR）系统。实验结果表明，所提出的神经定位流程实现的UE定位精度，接近于需要外部高精度真实位置标签进行训练的最先进方法。