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

Faizan Rauf; Srijita Sanyal; Markus Heinrichs; Aydin Sezgin

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arXiv · 2605.10352

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

Faizan Rauf·Srijita Sanyal·Markus Heinrichs·Aydin Sezgin

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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.

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Faizan Rauf, Srijita Sanyal, Markus Heinrichs, Aydin Sezgin. 2026-05-11. Quantifying System Level KPI Deviations of Sionna RT: Material and Near-Field Error Analysis Using a 5G OAI Testbed. https://arxiv.org/abs/2605.10352

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