🤖 AI Summary
This study addresses a critical gap in communication-aware robotic planning, where existing approaches commonly rely on channel-level metrics to predict end-to-end 5G throughput—a practice lacking empirical validation in private 5G deployments. Conducted in a shielded underground industrial environment, the work integrates commercial ray-tracing simulations, Gaussian process regression with a rational quadratic kernel, a mobile robotic platform, and off-the-shelf 5G user equipment to perform real-world measurements. It reveals for the first time that dynamic adaptation of MIMO spatial layers is the primary cause of systematic overestimation of throughput by conventional channel models, with ray tracing significantly overpredicting performance even in line-of-sight conditions. In contrast, a data-driven approach that directly learns end-to-end throughput reduces prediction error by approximately two-thirds and exhibits near-zero bias, demonstrating clear superiority over traditional channel-centric modeling.
📝 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.