Quantifying System Level KPI Deviations of Sionna RT: Material and Near-Field Error Analysis Using a 5G OAI Testbed

TL;DR

This study quantifies how ray tracing errors affect key 5G system performance metrics by comparing simulations with real hardware measurements, highlighting near-field effects and material mismatches.

Contribution

It provides a quantitative analysis of system-level KPI deviations caused by RT errors in a real 5G testbed, bridging the gap between simulation and hardware performance.

Findings

Near-field transition effects significantly impact KPI accuracy.

Material property mismatches contribute to channel modeling errors.

Quantitative benchmarks for digital twin-based 5G network planning are established.

Abstract

Ray tracing (RT) has recently gained renewed interest in wireless communications, driven by its integration into digital twin (DT) frameworks for site specific channel modeling. Several previous studies have validated RT at the channel level, yet how these errors propagate into real 5G system level key performance indicators (KPIs) on actual hardware remains unquantified. This paper addresses this gap by comparing Sionna RT simulated channels against vector network analyzer (VNA) measured channels using an OpenAirInterface (OAI) 5G NR testbed. Channel measurements are conducted at 20 receiver positions in an indoor laboratory, with both channel types injected into a hardware in the loop channel emulator interfacing an OAIBOX MAX base station and a Quectel UE. RSRP, PUCCH SNR, and SINR are evaluated under both conditions. The results identify antenna near-field transition effects as a critical position-dependent error source, alongside material property mismatch, providing a quantitative benchmark for digital twin-based 5G and beyond network planning.