AI 中文总结
本文基于上行链路探测参考信号，在OpenAirInterface 5G无线接入网实现实时室内定位系统，利用定位xApp从SRS测量得出CFR测量值，经转换和随机森林回归器估计用户设备二维位置，实验验证了该方法在OAI上的可行性与局限性。
AI 中文摘要
室内定位是未来5G-Advanced和6G系统的重要服务之一。本文提出了一种基于上行链路探测参考信号（SRS）的实时室内定位系统，该系统在OpenAirInterface（OAI）5G无线接入网（RAN）上实现。所提系统使用定位xApp从上行链路SRS测量中得出信道频率响应（CFR）测量值。SRS测量值通过标准化E2接口上的E2下层控制服务模型（E2SM-LLC）从gNB获取。xApp将CFR转换为32维物理感知特征向量，并使用随机森林（RF）回归器估计用户设备的二维位置。在EURECOM的多径丰富的室内实验室中，在基于OAI的5G测试平台上实现了定位xApp以验证所提系统。实验结果表明，在随机训练-测试评估下，所提系统的平均绝对误差（MAE）为0.12米。这些结果证明了基于上行链路SRS的实时室内定位在OAI上的可行性和局限性。
英文摘要
Indoor localization is one of the important services for future 5G-Advanced and 6G systems. This paper presents an uplink Sounding Reference Signal (SRS)-based real-time indoor localization system implemented over an OpenAirInterface (OAI) 5G Radio Access Network (RAN). The proposed system uses a Positioning xApp to derive Channel Frequency Response (CFR) measurements from uplink SRS measurements. The SRS measurements are obtained from the gNB through the E2 Service Model for Lower Layer Control (E2SM-LLC) over the standardized E2 interface. The xApp transforms the CFR into a 32-dimensional physics-aware feature vector and uses a Random Forest (RF) regressor to estimate the two-dimensional position of the user equipment. We implemented the Positioning xApp on an OAI-based 5G testbed in a multipath-rich indoor laboratory at EURECOM to validate the proposed system. Experimental results show that the proposed system achieves a mean absolute error (MAE) of 0.12 m under random train-test evaluation. These results demonstrate the feasibility and limitations of uplink SRS-based real-time indoor localization over OAI.
CommentsThis paper is accepted by ICMIC 2026
