Problem
Research questions and friction points this paper is trying to address.
indoor localization
Sounding Reference Signal
5G
real-time positioning
OpenAirInterface
Uplink SRS-Based Real-Time Indoor Localization System over OpenAirInterface
🤖 AI Summary
This work addresses the demand for high-precision real-time indoor positioning in 5G-Advanced and 6G systems by presenting the first end-to-end uplink SRS-based positioning system implemented on the OpenAirInterface 5G RAN platform. Leveraging the E2 interface and the E2SM-LLC service model, the system extracts channel frequency response data to construct physics-informed feature vectors, which are then processed by a random forest regression model to estimate the two-dimensional location of user equipment in real time. By integrating physical-layer measurements with machine learning, the proposed approach achieves a mean absolute positioning error of 0.12 meters in multipath-rich indoor environments, demonstrating both its feasibility and performance potential.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
SRS-based localization
OpenAirInterface
E2 interface
physics-aware feature
Random Forest regressor
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