AoA Services in 5G Networks: A Framework for Real-World Implementation and Systematic Testing

TL;DR

This paper introduces an open-source 5G testbed for AoA estimation that enables systematic testing of lightweight, network-native localization methods with high accuracy in realistic conditions.

Contribution

It presents the first fully open-source 5G testbed for AoA estimation, integrating advanced hardware and calibration techniques for realistic and repeatable experiments.

Findings

Achieved sub-degree to few-degree localization accuracy.

Validated the feasibility of network-native AoA-based localization.

Demonstrated the testbed's capability under realistic channel conditions.

Abstract

Accurate positioning is a key enabler for emerging 5G applications. While the standardized Location Management Function (LMF) operates centrally within the core network, its scalability and latency limitations hinder low-latency and fine-grained localization. A practical alternative is to shift positioning intelligence toward the radio access network (RAN), where uplink sounding reference signal (SRS)-based angle-of-arrival (AoA) estimation offers a lightweight, network-native solution. In this work, we present the first fully open-source 5G testbed for AoA estimation, enabling systematic and repeatable experimentation under realistic yet controllable channel conditions. The framework integrates the NVIDIA Sionna RT with a Keysight PROPSIM channel emulator and includes a novel phase calibration procedure for USRP N310 devices. Experimental results show sub-degree to few-degree accuracy, validating the feasibility of lightweight, single-anchor, network-native localization within next-generation 5G systems.