Why Channel-Centric Models are not Enough to Predict End-to-End Performance in Private 5G: A Measurement Campaign and Case Study

TL;DR

It is demonstrated that favorable channel conditions do not guarantee high throughput; communication-aware planners relying solely on channel-centric predictions risk overly optimistic trajectories that violate reliability requirements.

Abstract

Communication-aware robot planning requires accurate predictions of wireless network performance. Current approaches rely on channel-level metrics such as received signal strength and signal-to-noise ratio, assuming these translate reliably into end-to-end throughput. We challenge this assumption through a measurement campaign in a private 5G industrial environment. We evaluate throughput predictions from a commercial ray-tracing simulator as well as data-driven Gaussian process regression models against measurements collected using a mobile robot. The study uses off-the-shelf user equipment in an underground, radio-shielded facility with detailed 3D modeling, representing a best-case scenario for prediction accuracy. The ray-tracing simulator captures the spatial structure of indoor propagation and predicts channel-level metrics with reasonable fidelity. However, it systematically over-predicts throughput, even in line-of-sight regions. The dominant error source is shown to be over-estimation of sustainable MIMO spatial layers: the simulator assumes near-uniform four-layer transmission while measurements reveal substantial adaptation between one and three layers. This mismatch inflates predicted throughput even when channel metrics appear accurate. In contrast, a Gaussian process model with a rational quadratic kernel achieves approximately two-thirds reduction in prediction error with near-zero bias by learning end-to-end throughput directly from measurements. These findings demonstrate that favorable channel conditions do not guarantee high throughput; communication-aware planners relying solely on channel-centric predictions risk overly optimistic trajectories that violate reliability requirements. Accurate throughput prediction for 5G systems requires either extensive calibration of link-layer models or data-driven approaches that capture real system behavior.

Figures (10)

• Figure 1: Photograph of the testbed site; an underground, radio-shielded facility used for edge- and cloud-computing research. Photo © JannLipka2008Reaktor1KTHEntranceView360JannLipka2008Reaktor1KTHEntranceView360, used with permission. Source: 360Cities JannLipka2008Reaktor1KTHEntranceView360.
• Figure 2: Overview of the experimental and modeling workflow.
• Figure 3: 3D model of the KTH Reactor Hall imported into Altair Feko for radio propagation simulation.
• Figure 4: Plan view of the Reactor Hall showing the placement of all radio dots in the environment. Only the highlighted dot (bottom-left) was enabled for the measurement campaign.
• Figure 5: Site-wide 2D map of throughput: simulation vs. measurement and associated errors. A positive error indicates over-prediction.