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

2025-10-20

AI Summary

To address scalability and latency bottlenecks of the centralized 5G Location Management Function (LMF), this paper proposes a network-native, lightweight RAN-side positioning architecture. The core method offloads Angle-of-Arrival (AoA) estimation to base stations and leverages uplink Sounding Reference Signals (SRS) for single-anchor, low-overhead positioning. We introduce the first fully open-source 5G AoA testbed, design a novel phase calibration mechanism tailored for USRP N310 hardware, and integrate NVIDIA Sionna RT with Keysight PROPSIM for systematic, controlled-channel validation. Experimental results on a real software-defined radio (SDR) platform demonstrate sub-degree to several-degree AoA estimation accuracy, significantly reducing end-to-end positioning latency. The work validates the feasibility and practicality of RAN-native positioning for 5G-Advanced and 6G systems.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• Addressing scalability and latency limitations in 5G positioning
• Implementing lightweight AoA estimation in radio access networks
• Enabling systematic testing of network-native localization solutions

Innovation

• Open-source testbed enables systematic AoA experimentation
• Framework integrates Sionna RT with channel emulator
• Novel phase calibration procedure for USRP devices